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Search results “Web usage mining algorithms define”
Web Mining - Tutorial
 
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Web Mining Web Mining is the use of Data mining techniques to automatically discover and extract information from World Wide Web. There are 3 areas of web Mining Web content Mining. Web usage Mining Web structure Mining. Web content Mining Web content Mining is the process of extracting useful information from content of web document.it may consists of text images,audio,video or structured record such as list & tables. screen scaper,Mozenda,Automation Anywhere,Web content Extractor, Web info extractor are the tools used to extract essential information that one needs. Web Usage Mining Web usage Mining is the process of identifying browsing patterns by analysing the users Navigational behaviour. Techniques for discovery & pattern analysis are two types. They are Pattern Analysis Tool. Pattern Discovery Tool. Data pre processing,Path Analysis,Grouping,filtering,Statistical Analysis, Association Rules,Clustering,Sequential Pattterns,classification are the Analysis done to analyse the patterns. Web structure Mining Web structure Mining is a tool, used to extract patterns from hyperlinks in the web. Web structure Mining is also called link Mining. HITS & PAGE RANK Algorithm are the Popular Web structure Mining Algorithm. By applying Web content mining,web structure Mining & Web usage Mining knowledge is extracted from web data.
Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
 
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Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 51733 Well Academy
Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 29510 Red Apple Tutorials
What is STRUCTURE MINING? What does STRUCTURE MINING mean? STRUCTURE MINING meaning & explanation
 
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What is STRUCTURE MINING? What does STRUCTURE MINING mean? STRUCTURE MINING meaning - STRUCTURE MINING definition - STRUCTURE MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Structure mining or structured data mining is the process of finding and extracting useful information from semi-structured data sets. Graph mining, sequential pattern mining and molecule mining are special cases of structured data mining. The growth of the use of semi-structured data has created new opportunities for data mining, which has traditionally been concerned with tabular data sets, reflecting the strong association between data mining and relational databases. Much of the world's interesting and mineable data does not easily fold into relational databases, though a generation of software engineers have been trained to believe this was the only way to handle data, and data mining algorithms have generally been developed only to cope with tabular data. XML, being the most frequent way of representing semi-structured data, is able to represent both tabular data and arbitrary trees. Any particular representation of data to be exchanged between two applications in XML is normally described by a schema often written in XSD. Practical examples of such schemata, for instance NewsML, are normally very sophisticated, containing multiple optional subtrees, used for representing special case data. Frequently around 90% of a schema is concerned with the definition of these optional data items and sub-trees. Messages and data, therefore, that are transmitted or encoded using XML and that conform to the same schema are liable to contain very different data depending on what is being transmitted. Such data presents large problems for conventional data mining. Two messages that conform to the same schema may have little data in common. Building a training set from such data means that if one were to try to format it as tabular data for conventional data mining, large sections of the tables would or could be empty. There is a tacit assumption made in the design of most data mining algorithms that the data presented will be complete. The other necessity is that the actual mining algorithms employed, whether supervised or unsupervised, must be able to handle sparse data. Namely, machine learning algorithms perform badly with incomplete data sets where only part of the information is supplied. For instance methods based on neural networks. or Ross Quinlan's ID3 algorithm. are highly accurate with good and representative samples of the problem, but perform badly with biased data. Most of times better model presentation with more careful and unbiased representation of input and output is enough. A particularly relevant area where finding the appropriate structure and model is the key issue is text mining. XPath is the standard mechanism used to refer to nodes and data items within XML. It has similarities to standard techniques for navigating directory hierarchies used in operating systems user interfaces. To data and structure mine XML data of any form, at least two extensions are required to conventional data mining. These are the ability to associate an XPath statement with any data pattern and sub statements with each data node in the data pattern, and the ability to mine the presence and count of any node or set of nodes within the document. As an example, if one were to represent a family tree in XML, using these extensions one could create a data set containing all the individuals in the tree, data items such as name and age at death, and counts of related nodes, such as number of children. More sophisticated searches could extract data such as grandparents' lifespans etc. The addition of these data types related to the structure of a document or message facilitates structure mining.
Views: 383 The Audiopedia
SEO - Keyword discovery tool - Mozenda Data Mining - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 77 Data Analytics
What is Web Mining
 
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Views: 13534 TechGig
Answers from Big Data - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 254 Data Analytics
System Event Mining: Algorithms and Applications part 2
 
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Authors: Genady Ya. Grabarnik, St. John's University Larisa Shwartz, IBM Thomas J. Watson Research Center Tao Li, Florida International University Abstract: Many systems, from computing systems, physical systems, business systems, to social systems, are only observable indirectly from the events they emit. Events can be defined as real-world occurrences and they typically involve changes of system states. Events are naturally temporal and are often stored as logs, e.g., business transaction logs, stock trading logs, sensor logs, computer system logs, HTTP requests, database queries, network traffic data, etc. These events capture system states and activities over time. For effective system management, a system needs to automatically monitor, characterize, and understand its behavior and dynamics, mine events to uncover useful patterns, and acquire the needed knowledge from historical log/event data. Event mining is a series of techniques for automatically and efficiently extracting valuable knowledge from historical event/log data and plays an important role in system management. The purpose of this tutorial is to present a variety of event mining approaches and applications with a focus on computing system management. It is mainly intended for researchers, practitioners, and graduate students who are interested in learning about the state of the art in event mining. Link to tutorial: https://users.cs.fiu.edu/~taoli/event-mining/ More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 46 KDD2017 video
Analysis and prediction of Ε customers' behavior by mining clickstream data
 
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Analysis and prediction of Ε-customers' behavior by mining clickstream data Abstract: In a regular retail shop the behavior of customers may yield a lot to the shop assistant. However, when it comes to online shopping it is not possible to see and analyze customer behavior such as facial mimics, products they check or touch etc. In this case, clickstreams or the mouse movements of e-customers may provide some hints about their buying behavior. In this study, we have presented a model to analyze clickstreams of e-customers and extract information and make predictions about their shopping behavior on a digital market place. After collecting data from an e-commerce market in Turkey, we performed a data mining application and extracted online customers' behavior patterns about buying or not. The model we present predicts whether customers will or will not buy their items added to shopping baskets on a digital market place. For the analysis, decision tree and multi-layer neural network prediction data mining models have been used. Findings have been discussed in the conclusion
Views: 603 1 Crore Projects
Neural Networks in Data Mining | MLP Multi layer Perceptron Algorithm in Data Mining
 
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Classification is a predictive modelling. Classification consists of assigning a class label to a set of unclassified cases Steps of Classification: 1. Model construction: Describing a set of predetermined classes Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute. The set of tuples used for model construction is training set. The model is represented as classification rules, decision trees, or mathematical formulae. 2. Model usage: For classifying future or unknown objects Estimate accuracy of the model If the accuracy is acceptable, use the model to classify new data MLP- NN Classification Algorithm The MLP-NN algorithm performs learning on a multilayer feed-forward neural network. It iteratively learns a set of weights for prediction of the class label of tuples. A multilayer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer. Each layer is made up of units. The inputs to the network correspond to the attributes measured for each training tuple. The inputs are fed simultaneously into the units making up the input layer. These inputs pass through the input layer and are then weighted and fed simultaneously to a second layer of “neuronlike” units, known as a hidden layer. The outputs of the hidden layer units can be input to another hidden layer, and so on. The number of hidden layers is arbitrary, although in practice, usually only one is used. The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network’s prediction for given tuples. Algorithm of MLP-NN is as follows: Step 1: Initialize input of all weights with small random numbers. Step 2: Calculate the weight sum of the inputs. Step 3: Calculate activation function of all hidden layer. Step 4: Output of all layers For more information and query visit our website: Website : http://www.e2matrix.com Blog : http://www.e2matrix.com/blog/ WordPress : https://teche2matrix.wordpress.com/ Blogger : https://teche2matrix.blogspot.in/ Contact Us : +91 9041262727 Follow Us on Social Media Facebook : https://www.facebook.com/etwomatrix.researchlab Twitter : https://twitter.com/E2MATRIX1 LinkedIn : https://www.linkedin.com/in/e2matrix-training-research Google Plus : https://plus.google.com/u/0/+E2MatrixJalandhar Pinterest : https://in.pinterest.com/e2matrixresearchlab/ Tumblr : https://www.tumblr.com/blog/e2matrix24
Web Data Mining
 
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Data mining tools for getting similarity and classification among different websites.(Naive Bayes Classifier, k-means,others)
Views: 123 Juan Carlos Ucles
text mining, web mining and sentiment analysis
 
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text mining, web mining
Views: 1542 Kakoli Bandyopadhyay
link mining
 
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Subscribe today and give the gift of knowledge to yourself or a friend link mining Link Mining . Lise Getoor Department of Computer Science University of Maryland, College Park. Link Mining. Traditional machine learning and data mining approaches assume: A random sample of homogeneous objects from single relation Real world data sets: Slideshow 2979172 by zaina show1 : Link mining show2 : Link mining1 show3 : Outline show4 : Linked data show5 : Sample domains show6 : Example linked bibliographic data show7 : Link mining tasks show8 : Link based object classification show9 : Link type show10 : Predicting link existence show11 : Link cardinality estimation i show12 : Link cardinality estimation ii show13 : Object identity show14 : Link mining challenges show15 : Logical vs statistical dependence show16 : Model search show17 : Feature construction show18 : Aggregation show19 : Selection show20 : Individuals vs classes show21 : Instance based dependencies show22 : Class based dependencies show23 : Collective classification show24 : Model selection estimation show25 : Collective classification algorithm show26 : Collective classification algorithm1 show27 : Labeled unlabeled data show28 : Link mining show29 : Link prior probability show30 : Summary show31 : References
Views: 126 Magalyn Melgarejo
Web Mining
 
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Web Mining
Views: 310 Blind Bakhtyar
40 Data Analysis New Tools - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 89 Data Analytics
What is WEB CONTENT? What doe WEB CONTENT mean? WEB CONTENT meaning & explanation
 
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What is WEB CONTENT? What doe WEB CONTENT mean? WEB CONTENT meaning - WEB CONTENT definition - WEB CONTENT explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Web content is the textual, visual, or aural content that is encountered as part of the user experience on websites. It may include—among other things—text, images, sounds, videos, and animations. In Information Architecture for the World Wide Web, Lou Rosenfeld and Peter Morville write, "We define content broadly as 'the stuff in your Web site.' This may include documents, data, applications, e-services, images, audio and video files, personal Web pages, archived e-mail messages, and more. And we include future stuff as well as present stuff." While the Internet began with a U.S. Government research project in the late 1950s, the web in its present form did not appear on the Internet until after Tim Berners-Lee and his colleagues at the European laboratory (CERN) proposed the concept of linking documents with hypertext. But it was not until Mosaic, the forerunner of the famous Netscape Navigator, appeared that the Internet become more than a file serving system. The use of hypertext, hyperlinks, and a page-based model of sharing information, introduced with Mosaic and later Netscape, helped to define web content, and the formation of websites. Today, we largely categorize websites as being a particular type of website according to the content a website contains. Web content is dominated by the "page" concept, its beginnings in an academic setting, and in a setting dominated by type-written pages, the idea of the web was to link directly from one academic paper to another academic paper. This was a completely revolutionary idea in the late 1980s and early 1990s when the best a link could be made was to cite a reference in the midst of a type written paper and name that reference either at the bottom of the page or on the last page of the academic paper. When it was possible for any person to write and own a Mosaic page, the concept of a "home page" blurred the idea of a page. It was possible for anyone to own a "Web page" or a "home page" which in many cases the website contained many physical pages in spite of being called "a page". People often cited their "home page" to provide credentials, links to anything that a person supported, or any other individual content a person wanted to publish. Even though we may embed various protocols within web pages, the "web page" composed of "HTML" (or some variation) content is still the dominant way whereby we share content. And while there are many web pages with localized proprietary structure (most usually, business websites), many millions of websites abound that are structured according to a common core idea. Blogs are a type of website that contain mainly web pages authored in HTML (although the blogger may be totally unaware that the web pages are composed using HTML due to the blogging tool that may be in use). Millions of people use blogs online; a blog is now the new "home page", that is, a place where a persona can reveal personal information, and/or build a concept as to who this persona is. Even though a blog may be written for other purposes, such as promoting a business, the core of a blog is the fact that it is written by a "person" and that person reveals information from her/his perspective. Blogs have become a very powerful weapon used by content marketers who desire to increase their site's traffic, as well as, rank in the search engine result pages (SERPs). In fact, new research from Technorati shows that blogs now outrank social networks for consumer influence (Technorati’s 2013 Digital Influence Report data).
Views: 398 The Audiopedia
K Means Clustering Algorithm | K Means Example in Python | Machine Learning Algorithms | Edureka
 
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** Python Training for Data Science: https://www.edureka.co/python ** This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session: 1. What is Clustering? 2. Types of Clustering 3. What is K-Means Clustering? 4. How does a K-Means Algorithm works? 5. K-Means Clustering Using Python Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm Subscribe to our channel to get video updates. Hit the subscribe button above. How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Programmatically download and analyze data 2. Learn techniques to deal with different types of data – ordinal, categorical, encoding 3. Learn data visualization 4. Using I python notebooks, master the art of presenting step by step data analysis 5. Gain insight into the 'Roles' played by a Machine Learning Engineer 6. Describe Machine Learning 7. Work with real-time data 8. Learn tools and techniques for predictive modeling 9. Discuss Machine Learning algorithms and their implementation 10. Validate Machine Learning algorithms 11. Explain Time Series and its related concepts 12. Perform Text Mining and Sentimental analysis 13. Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Review Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."
Views: 27416 edureka!
How To Connect Google Webmaster Tools To Google Analytics - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 83 Data Analytics
DATA MINING   2 Text Retrieval and Search Engines   1 1 1 Course Welcome Video
 
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https://www.coursera.org/learn/text-retrieval
Views: 637 Ryo Eng
Web Crawler - CS101 - Udacity
 
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Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/f16/ Sergey Brin, co-founder of Google, introduces the class. What is a web-crawler and why do you need one? All units in this course below: Unit 1: http://www.youtube.com/playlist?list=PLF6D042E98ED5C691 Unit 2: http://www.youtube.com/playlist?list=PL6A1005157875332F Unit 3: http://www.youtube.com/playlist?list=PL62AE4EA617CF97D7 Unit 4: http://www.youtube.com/playlist?list=PL886F98D98288A232& Unit 5: http://www.youtube.com/playlist?list=PLBA8DEB5640ECBBDD Unit 6: http://www.youtube.com/playlist?list=PL6B5C5EC17F3404D6 Unit 7: http://www.youtube.com/playlist?list=PL6511E7098EC577BE OfficeHours 1: http://www.youtube.com/playlist?list=PLDA5F9F71AFF4B69E Join the class at http://www.udacity.com to gain access to interactive quizzes, homework, programming assignments and a helpful community.
Views: 124923 Udacity
Web Personalization based on Usage Mining part 2
 
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By : Ahmed Hamdy Ali
Views: 245 Ahmed Emara
What is CLICKSTREAM? What does CLICKSTREAM mean? CLICKSTREAM meaning, definition & explanation
 
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What is CLICKSTREAM? What does CLICKSTREAM mean? CLICKSTREAM meaning - CLICKSTREAM pronunciation - CLICKSTREAM definition - CLICKSTREAM explanation - How to pronounce CLICKSTREAM? Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ A clickstream is the recording of the parts of the screen a computer user clicks on while web browsing or using another software application. As the user clicks anywhere in the webpage or application, the action is logged on a client or inside the web server, as well as possibly the web browser, router, proxy server or ad server. Clickstream analysis is useful for web activity analysis, software testing, market research, and for analyzing employee productivity. Initial clickstream or click path data had to be gleaned from server log files. Because human and machine traffic were not differentiated, the study of human clicks took a substantial effort. Subsequently, Javascript technologies were developed which use a tracking cookie to generate a series of signals from browsers. In other words, information was only collected from "real humans" clicking on sites through browsers.It was not possible to identify the clickpath. A clickstream is a series of page requests, every page requested generates a signal. These signals can be graphically represented for clickstream reporting. The main point of clickstream tracking is to give webmasters insight into what visitors on their site are doing. This data itself is "neutral" in the sense that any dataset is neutral. The data can be used in various scenarios, one of which is marketing. Additionally, any webmaster, researcher, blogger or person with a website can learn about how to improve their site. Use of clickstream data can raise privacy concerns, especially since some Internet service providers have resorted to selling users' clickstream data as a way to enhance revenue. There are 10-12 companies that purchase this data, typically for about $0.40/month per user. While this practice may not directly identify individual users, it is often possible to indirectly identify specific users, an example being the AOL search data scandal. Most consumers are unaware of this practice, and its potential for compromising their privacy. In addition, few ISPs publicly admit to this practice. Analyzing the data of clients that visit a company website can be important in order to remain competitive. This analysis can be used to generate two findings for the company, the first being an analysis of a user’s clickstream while using a website to reveal usage patterns, which in turn gives a heightened understanding of customer behaviour. This use of the analysis creates a user profile that aids in understanding the types of people that visit a company’s website. As discussed in Van den Poel & Buckinx (2005), clickstream analysis can be used to predict whether a customer is likely to purchase from an e-commerce website. Clickstream analysis can also be used to improve customer satisfaction with the website and with the company itself. This can generate a business advantage, and be used to assess the effectiveness of advertising on a web page or site. Data mining, column-oriented DBMS, and integrated OLAP systems can be used in conjunction with clickstreams to better record and analyze this data. Clickstreams can also be used to allow the user to see where they have been and allow them to easily return to a page they have already visited, a function that is already incorporated in most browsers. Unauthorized clickstream data collection is considered to be spyware. However, authorized clickstream data collection comes from organizations that use opt-in panels to generate market research using panelists who agree to share their clickstream data with other companies by downloading and installing specialized clickstream collection agents.
Views: 1091 The Audiopedia
What is the world wide web? - Twila Camp
 
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View full lesson: http://ed.ted.com/lessons/what-is-the-world-wide-web-twila-camp The world wide web is used every day by millions of people for everything from checking the weather to sharing cat videos. But what is it exactly? Twila Camp describes this interconnected information system as a virtual city that everyone owns and explains how it's organized in a way that mimics our brain's natural way of thinking. Lesson by Twila Camp, animation by Flaming Medusa Studios Inc.
Views: 459821 TED-Ed
BigDataX: Structure of the web
 
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Big Data Fundamentals is part of the Big Data MicroMasters program offered by The University of Adelaide and edX. Learn how big data is driving organisational change and essential analytical tools and techniques including data mining and PageRank algorithms. Enrol now! http://bit.ly/2rg1TuF
What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning
 
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What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning - SOCIAL MEDIA MINING definition - SOCIAL MEDIA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Social media mining is the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. The term "mining" is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to sift through vast quanitites of raw ore to find the precious minerals; likewise, social media "mining" requires human data analysts and automated software programs to sift through massive amounts of raw social media data (e.g., on social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, etc.) in order to discern patterns and trends. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs (or, for companies, new products, processes and services). Social media mining uses a range of basic concepts from computer science, data mining, machine learning and statistics. Social media miners develop algorithms suitable for investigating massive files of social media data. Social media mining is based on theories and methodologies from social network analysis, network science, sociology, ethnography, optimization and mathematics. It encompasses the tools to formally represent, measure, model, and mine meaningful patterns from large-scale social media data. In the 2010s, major corporations, as well as governments and not-for-profit organizations engage in social media mining to find out more about key populations of interest, which, depending on the organization carrying out the "mining", may be customers, clients, or citizens. As defined by Kaplan and Haenlein, social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content." There are many categories of social media including, but not limited to, social networking (Facebook or LinkedIn), microblogging (Twitter), photo sharing (Flickr, Photobucket, or Picasa), news aggregation (Google reader, StumbleUpon, or Feedburner), video sharing (YouTube, MetaCafe), livecasting (Ustream or Twitch.tv), virtual worlds (Kaneva), social gaming (World of Warcraft), social search (Google, Bing, or Ask.com), and instant messaging (Google Talk, Skype, or Yahoo! messenger). The first social media website was introduced by GeoCities in 1994. It enabled users to create their own homepages without having a sophisticated knowledge of HTML coding. The first social networking site, SixDegree.com, was introduced in 1997. Since then, many other social media sites have been introduced, each providing service to millions of people. These individuals form a virtual world in which individuals (social atoms), entities (content, sites, etc.) and interactions (between individuals, between entities, between individuals and entities) coexist. Social norms and human behavior govern this virtual world. By understanding these social norms and models of human behavior and combining them with the observations and measurements of this virtual world, one can systematically analyze and mine social media. Social media mining is the process of representing, analyzing, and extracting meaningful patterns from data in social media, resulting from social interactions. It is an interdisciplinary field encompassing techniques from computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions. In addition, social media mining provides necessary tools to mine this world for interesting patterns, analyze information diffusion, study influence and homophily, provide effective recommendations, and analyze novel social behavior in social media.
Views: 867 The Audiopedia
What is Data Mining?
 
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NJIT School of Management professor Stephan P Kudyba describes what data mining is and how it is being used in the business world.
Views: 406227 YouTube NJIT
What is KNOWLEDGE DISCOVERY? What does KNOWLEDGE DISCOVERY mean? KNOWLEDGE DISCOVERY meaning
 
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What is KNOWLEDGE DISCOVERY? What does KNOWLEDGE DISCOVERY mean? KNOWLEDGE DISCOVERY meaning - KNOWLEDGE DISCOVERY definition - KNOWLEDGE DISCOVERY explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. nowledge discovery describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data. It is often described as deriving knowledge from the input data. Knowledge discovery developed out of the data mining domain, and is closely related to it both in terms of methodology and terminology. The most well-known branch of data mining is knowledge discovery, also known as knowledge discovery in databases (KDD). Just as many other forms of knowledge discovery it creates abstractions of the input data. The knowledge obtained through the process may become additional data that can be used for further usage and discovery. Often the outcomes from knowledge discovery are not actionable, actionable knowledge discovery, also known as domain driven data mining, aims to discover and deliver actionable knowledge and insights. Another promising application of knowledge discovery is in the area of software modernization, weakness discovery and compliance which involves understanding existing software artifacts. This process is related to a concept of reverse engineering. Usually the knowledge obtained from existing software is presented in the form of models to which specific queries can be made when necessary. An entity relationship is a frequent format of representing knowledge obtained from existing software. Object Management Group (OMG) developed specification Knowledge Discovery Metamodel (KDM) which defines an ontology for the software assets and their relationships for the purpose of performing knowledge discovery of existing code. Knowledge discovery from existing software systems, also known as software mining is closely related to data mining, since existing software artifacts contain enormous value for risk management and business value, key for the evaluation and evolution of software systems. Instead of mining individual data sets, software mining focuses on metadata, such as process flows (e.g. data flows, control flows, & call maps), architecture, database schemas, and business rules/terms/process.
Views: 2004 The Audiopedia
How does a blockchain work - Simply Explained
 
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What is a blockchain and how do they work? I'll explain why blockchains are so special in simple and plain English! 💰 Want to buy Bitcoin or Ethereum? Buy for $100 and get $10 free (through my affiliate link): https://www.coinbase.com/join/59284524822a3d0b19e11134 📚 Sources can be found on my website: https://www.savjee.be/videos/simply-explained/how-does-a-blockchain-work/ 🐦 Follow me on Twitter: https://twitter.com/savjee ✏️ Check out my blog: https://www.savjee.be ✉️ Subscribe to newsletter: https://goo.gl/nueDfz 👍🏻 Like my Facebook page: https://www.facebook.com/savjee
Views: 2625871 Simply Explained - Savjee
Data Collection and Preprocessing | Lecture 6
 
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Deep Learning Crash Course playlist: https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07 Highlights: Garbage-in, Garbage-out Dataset Bias Data Collection Web Mining Subjective Studies Data Imputation Feature Scaling Data Imbalance #deeplearning #machinelearning
Views: 1237 Leo Isikdogan
Web data extractor & data mining- Handling Large Web site Item | Excel data Reseller & Dropship
 
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Web scraping web data extractor is a powerful data, link, url, email tool popular utility for internet marketing, mailing list management, site promotion and 2 discover extractor, the scraper that captures alternative from any website social media sites, or content area on if you are interested fully managed extraction service, then check out promptcloud's services. Use casesweb data extractor extracting and parsing github wanghaisheng awesome web a curated list webextractor360 open source codeplex archive. It uses regular expressions to find, extract and scrape internet data quickly easily. Whether seeking urls, phone numbers, 21 web data extractor is a scraping tool specifically designed for mass gathering of various types. Web scraping web data extractor extract email, url, meta tag, phone, fax from download. Web data extractor pro 3. It can be a url, meta tags with title, desc and 7. Extract url, meta tag (title, desc, keyword), body text, email, phone, fax from web site, search 27 data extractor can extract of different kind a given website. Web data extraction fminer. 1 (64 bit hidden web data extractor semantic scholar. It is very web data extractor pro a scraping tool specifically designed for mass gathering of various types. The software can harvest urls, extracting and parsing structured data with jquery selector, xpath or jsonpath from common web format like html, xml json a curated list of promising extractors resources webextractor360 is free open source extractor. It scours the internet finding and extracting all relative. Download the latest version of web data extractor free in english on how to use pro vimeo. It can harvest urls, web data extractor a powerful link utility. A powerful web data link extractor utility extract meta tag title desc keyword body text email phone fax from site search results or list of urls high page 1komal tanejashri ram college engineering, palwal gandhi1211 gmail mdu rohtak with extraction, you choose the content are looking for and program does rest. Web data extractor free download for windows 10, 7, 8. Custom crawling 27 2011 web data extractor promises to give users the power remove any important from a site. A deep dive into natural language processing (nlp) web data mining is divided three major groups content mining, structure and usage. Web mining wikipedia web is the application of data techniques to discover patterns from world wide. This survey paper reports the basic web mining aims to discover useful information or knowledge from hyperlink structure, page, and usage data. Web data mining, 2nd edition exploring hyperlinks, contents, and web mining not just on the software advice. Data mining in web applications. Web data mining exploring hyperlinks, contents, and usage in web applications what is mining? Definition from whatis searchcrm. Web data mining and applications in business intelligence web humboldt universitt zu berlin. Web mining aims to dis cover useful data and web are not the same thing. Extracting the rapid growth of web in past two decades has made it larg est publicly accessible data source world. Web mining wikipedia. The web is one of the biggest data sources to serve as input for mining applications. Web data mining exploring hyperlinks, contents, and usage web mining, book by bing liu uic computer sciencewhat is mining? Definition from techopedia. Most useful difference between data mining vs web. As the name proposes, this is information gathered by web mining aims to discover useful and knowledge from hyperlinks, page contents, usage data. Although web mining uses many is the process of using data techniques and algorithms to extract information directly from by extracting it documents 19 that are generated systems. Web data mining is based on ir, machine learning (ml), statistics web exploring hyperlinks, contents, and usage (data centric systems applications) [bing liu] amazon. Based on the primary kind of data used in mining process, web aims to discover useful information and knowledge from hyperlinks, page contents, usage. Data mining world wide web tutorialspoint.
Views: 262 CyberScrap youpul
Symmetric Key and Public Key Encryption
 
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Modern day encryption is performed in two different ways. Check out http://YouTube.com/ITFreeTraining or http://itfreetraining.com for more of our always free training videos. Using the same key or using a pair of keys called the public and private keys. This video looks at how these systems work and how they can be used together to perform encryption. Download the PDF handout http://itfreetraining.com/Handouts/Ce... Encryption Types Encryption is the process of scrambling data so it cannot be read without a decryption key. Encryption prevents data being read by a 3rd party if it is intercepted by a 3rd party. The two encryption methods that are used today are symmetric and public key encryption. Symmetric Key Symmetric key encryption uses the same key to encrypt data as decrypt data. This is generally quite fast when compared with public key encryption. In order to protect the data, the key needs to be secured. If a 3rd party was able to gain access to the key, they could decrypt any data that was encrypt with that data. For this reason, a secure channel is required to transfer the key if you need to transfer data between two points. For example, if you encrypted data on a CD and mail it to another party, the key must also be transferred to the second party so that they can decrypt the data. This is often done using e-mail or the telephone. In a lot of cases, sending the data using one method and the key using another method is enough to protect the data as an attacker would need to get both in order to decrypt the data. Public Key Encryption This method of encryption uses two keys. One key is used to encrypt data and the other key is used to decrypt data. The advantage of this is that the public key can be downloaded by anyone. Anyone with the public key can encrypt data that can only be decrypted using a private key. This means the public key does not need to be secured. The private key does need to be keep in a safe place. The advantage of using such a system is the private key is not required by the other party to perform encryption. Since the private key does not need to be transferred to the second party there is no risk of the private key being intercepted by a 3rd party. Public Key encryption is slower when compared with symmetric key so it is not always suitable for every application. The math used is complex but to put it simply it uses the modulus or remainder operator. For example, if you wanted to solve X mod 5 = 2, the possible solutions would be 2, 7, 12 and so on. The private key provides additional information which allows the problem to be solved easily. The math is more complex and uses much larger numbers than this but basically public and private key encryption rely on the modulus operator to work. Combing The Two There are two reasons you want to combine the two. The first is that often communication will be broken into two steps. Key exchange and data exchange. For key exchange, to protect the key used in data exchange it is often encrypted using public key encryption. Although slower than symmetric key encryption, this method ensures the key cannot accessed by a 3rd party while being transferred. Since the key has been transferred using a secure channel, a symmetric key can be used for data exchange. In some cases, data exchange may be done using public key encryption. If this is the case, often the data exchange will be done using a small key size to reduce the processing time. The second reason that both may be used is when a symmetric key is used and the key needs to be provided to multiple users. For example, if you are using encryption file system (EFS) this allows multiple users to access the same file, which includes recovery users. In order to make this possible, multiple copies of the same key are stored in the file and protected from being read by encrypting it with the public key of each user that requires access. References "Public-key cryptography" http://en.wikipedia.org/wiki/Public-k... "Encryption" http://en.wikipedia.org/wiki/Encryption
Views: 470405 itfreetraining
Click Stream Data Analysis
 
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This video about how clickstream data is gonna helpful in the e-commerce business
Views: 1362 Jayanth Gowda
Social Network Analysis
 
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An overview of social networks and social network analysis. See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
Views: 4262 Microsoft Research
Text Mining Tutorials for Beginners | Importance of Text Mining | Data Science Certification -ExcelR
 
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ExcelR: Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Things you will learn in this video 1) What is Text mining? 2) How clustering techniques helps and text data analysis? 3) What is word cloud? 4) Examples for text mining 5) Text mining terminology and pre-processing To buy eLearning course on Data Science click here https://goo.gl/oMiQMw To register for classroom training click here https://goo.gl/UyU2ve To Enroll for virtual online training click here " https://goo.gl/JTkWXo" SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For K-Means Clustering Tutorial click here https://goo.gl/PYqXRJ For Introduction to Clustering click here Introduction to Clustering | Cluster Analysis #ExcelRSolutions #Textmining #Whatistextmining #Textminingimportance #Wordcloud #DataSciencetutorial #DataScienceforbeginners #DataScienceTraining ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
How kNN algorithm works
 
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In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3. This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy
Views: 400322 Thales Sehn Körting
What is Hashing & Digital Signature in The Blockchain?
 
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What is Hashing & Digital Signature in The Blockchain? https://blockgeeks.com/ Today, we're going to be talking about the word blockchain and breaking it down to understand what does it mean when someone says, "Blockchain." What is hashing? Hashing refers to the concept of taking an arbitrary amount of input data, applying some algorithm to it, and generating a fixed-size output data called the hash. The input can be any number of bits that could represent a single character, an MP3 file, an entire novel, a spreadsheet of your banking history, or even the entire Internet. The point is that the input can be infinitely big. The hashing algorithm [00:01:00] can be chosen depending on your needs and there are many publicly available hashing algorithms. The point is that the algorithm takes the infinite input of bits, applies some calculations to them, and outputs a finite number of bits. For example, 256 bits. What can this hash be used for? A common usage for hashes today is to fingerprint files, also known as check zones. This means that a hash is used to verify that a file has not been [00:01:30] tampered with or modified in any way not intended by the author. If WikiLeaks, for example, publishes a set of files along with their MD5 hashes, whoever downloads those files can verify that they are actually from WikiLeaks by calculating the MD5 hash of the downloaded files, and if the hash doesn't match what was published by WikiLeaks, then you know that the file has been modified in some way. How does the blockchain make use of hashes? [00:02:00] Hashes are used in blockchains to represent the current state of the world. The input is the entire state of the blockchain, meaning all the transactions that have taken place so far and the resulting output hash represents the current state of the blockchain. The hash is used to agree between all parties that the world state is one in the same, but how are these hashes actually calculated? The first hash is calculated for the first block [00:02:30] or the Genesis block using the transactions inside that block. The sequence of initial transactions is used to calculate a block hash for the Genesis block. For every new block that is generated afterwords, the previous block's hash is also used, as well as its own transactions, as input to determine its block hash. This is how a chain of blocks is formed, each new block hash pointing to the block hash that came before it. This system of hashing guarantees that no transaction in the history can be tampered with because if any single part of the transaction changes, so does the hash of the block to which it belongs, and any following blocks' hashes as a result. It would be fairly easy to catch any tampering as a result because you can just compare the hashes. This is cool because everyone on the blockchain only needs to agree on 256 bits to represent the potentially infinite state of the blockchain. The Ethereum blockchain is currently tens of gigabytes, but the current state of the blockchain, as of this recording, is this hexadecimal hash representing 256 bits. What about digital signatures? Digital signatures, like real signatures, are a way to prove that somebody is who they say they are, except that we use cryptography or math, which is more secure than handwritten signatures that can be [00:04:00] easily forged. A digital signature is a way to prove that a message originates from a specific person and no one else, like a hacker. Digital signatures are used today all over the Internet. Whenever you visit a website over ACTPS, you are using SSL, which uses digital signatures to establish trust between you and the server. This means that when you visit Facebook.com, your browser can check the digital signature that came with the web page to verify that it indeed originated from Facebook and not some hacker. In asymmetric encryption systems, users generate something called a key pair, which is a public key and a private key using some known algorithm. The public key and private key are associated with each other through some mathematical relationship. The public key is meant to be distributed publicly to serve as an address to receive messages from other users, like an IP address or home address. The private key is meant to be kept secret and is used to digitally sign messages sent to other users. The signature is included with the message so that the recipient can verify using the sender's public key. This way, the recipient can be sure that only the sender could have sent this message. Generating a key pair is analogous to creating an account on the blockchain, but without having to actually register anywhere. Pretty cool. Also, every transaction that is executed on the blockchain is digitally signed by the sender using their private key. This signature ensures that only the owner of the account can move money out of the account.
Views: 24770 Blockgeeks
WDM 1:What is Data Mining
 
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Introduction to Data Mining For Full Course Experience Please Go To http://mentorsnet.org/course_preview?course_id=1 Full Course Experience Includes 1. Access to course videos and exercises 2. View & manage your progress/pace 3. In-class projects and code reviews 4. Personal guidance from your Mentors
Views: 41242 Oresoft LWC
sOnr Web Mining for Confluence - PoolParty Tutorial #23
 
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SONR IS A TOOL FOR MARKET OBSERVERS AND TREND SCOUTS (http://www.sonr-webmining.com/). With sOnr, you will keep track of everything that happens in a domain or industry of your interest. SONR IS BASED ON SEMANTIC TECHNOLOGIES. It is embedded in Atlassian Confluence, a highly useful collaboration platform. This architectural approach supports teams of market observers to extract relevant information from news services, blogs, and short messages automatically. SONR HELPS TO EXCHANGE IDEAS AND TO STRUCTURE KNOWLEDGE. A built-in semantic search engine is one of its core elements. Automatic agents crawl the web and the intranet. Collaborative features leverage the value of your findings! USERS WILL BENEFIT FROM - enterprise-readiness, - highly precise search results, - collaborative knowledge management, - a coffee break while sOnr is mining the web
What is web personalization?
 
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Learn more about web personalization and what it can do for you. https://www.persosa.com/whitepapers/what-is-personalization
Views: 529 Persosa
Online Data Mining Software www.fastdatascience.com V3
 
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Watch a free Web Application for Data Mining. You can try different Visualisation Methods and a lot of Modeling Techniques.
Views: 77 Fast Data Science
Discovering Content by Mining the Entity Web - Part 1 of 6
 
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Deep Dhillon, CTO of Evri.com presents Evri's technology to UW students at the Paul G. Allen Center for Computer Science & Engineering. Talk abstract: Unstructured natural language text found in blogs, news and other web content is rich with semantic relations linking entities (people, places and things). At Evri, we are building a system which automatically reads web content similar to the way humans do. The system can be thought of as an army of 7th grade grammar students armed with a really large dictionary. The dictionary, or knowledge base, consists of relatively static information mined from structured and semi-structured publicly available information repositories like Freebase, Wikipedia, and Amazon. This large knowledge base is in turn used by a highly distributed search and indexing infrastructure to perform a deep linguistic analysis of many millions of documents ultimately culminating in a large set of semantic relationships expressing grammatical SVO style clause level relationships. This highly expressive, exacting, and scalable index makes possible a new generation of content discovery applications. Need a custom machine learning solution like this one? Visit http://www.xyonix.com.
Views: 2166 zang0
Data Mining - Regression(Construction)
 
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SSK 4606 - Data Mining
Views: 99 syairah syak
What is VIDEO MOTION ANALYSIS? What does VIDEO MOTION ANALYSIS mean? VIDEO MOTION ANALYSIS meaning
 
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What is VIDEO MOTION ANALYSIS? What does VIDEO MOTION ANALYSIS mean? VIDEO MOTION ANALYSIS meaning - VIDEO MOTION ANALYSIS definition - VIDEO MOTION ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Video motion analysis is a technique used to get information about moving objects from video. Examples of this include gait analysis, sport replays, speed and acceleration calculations and, in the case of team or individual sports, task performance analysis. The motions analysis technique usually involves a high-speed camera and a computer that has software allowing frame-by-frame playback of the video. Traditionally, video motion analysis has been used in scientific circles for calculation of speeds of projectiles, or in sport for improving play of athletes. Recently, computer technology has allowed other applications of video motion analysis to surface including things like teaching fundamental laws of physics to school students, or general educational projects in sport and science. In sport, systems have been developed to provide a high level of task, performance and physiological data to coaches, teams and players. The objective is to improve individual and team performance and/or analyse opposition patterns of play to give tactical advantage. The repetitive and patterned nature of sports games lends itself to video analysis in that over a period of time real patterns, trends or habits can be discerned. Police and forensic scientists analyse CCTV video when investigating criminal activity. Police use software which performs video motion analysis to search for key events in video and find suspects. A digital video camera is mounted on a tripod. The moving object of interest is filmed doing a motion with a scale in clear view on the camera. Using video motion analysis software, the image on screen can be calibrated to the size of the scale enabling measurement of real world values. The software also takes note of the time between frames to give a movement versus time data set. This is useful in calculating gravity for instance from a dropping ball. Sophisticated sport analysis systems such as those by Verusco Technologies in New Zealand use other methods such as direct feeds from satellite television to provide real-time analysis to coaches over the Internet and more detailed post game analysis after the game has ended. There are many commercial packages that enable frame by frame or real-time video motion analysis. There are also free packages available that provide the necessary software functions. These free packages include the relatively old but still functional Physvis, and a relatively new program called PhysMo which runs on Macintosh and Windows. Upmygame is a free online application. VideoStrobe is free software that creates a strobographic image from a video; motion analysis can then be carried out with dynamic geometry software such as GeoGebra. The objective for video motion analysis will determine the type of software used. Prozone and Amisco are expensive stadium-based camera installations focusing on player movement and patterns. Both of these provide a service to "tag" or "code" the video with the players' actions, and deliver the results after the match. In each of these services, the data is tagged according to the company's standards for defining actions. Verusco Technologies are oriented more on task and performance and therefore can analyse games from any ground. Focus X2 and Sportscode systems rely on the team performing the analysis in house, allowing the results to be available immediately, and to the team's own coding standards. MatchMatix takes the data output of video analysis software and analyses sequences of events. Live HTML reports are generated and shared across a LAN, giving updates to the manager on the touchline while the game is in progress.
Views: 162 The Audiopedia
Getting Started with Orange 03: Widgets and Channels
 
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Orange data mining widgets and communication channels. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 53801 Orange Data Mining
Ashutosh Jadhav: Knowledge-driven Search Intent Mining
 
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http://www.knoesis.org/aboutus/thesis_defense#jadhav ABSTRACT: Understanding users’ latent intents behind search queries is essential for satisfying a user’s search needs. Search intent mining can help search engines to enhance its ranking of search results, enabling new search features like instant answers, personalization, search result diversification, and the recommendation of more relevant ads. Consequently, there has been increasing attention on studying how to effectively mine search intents by analyzing search engine query logs. While state-of-the-art techniques can identify the domain of the queries (e.g. sports, movies, health), identifying domain-specific intent is still an open problem. Among all the topics available on the Internet, health is one of the most important in terms of impact on the user and it is one of the most frequently searched areas. This dissertation presents a knowledge-driven approach for domain-specific search intent mining with a focus on health-related search queries. First, we identified 14 consumer-oriented health search intent classes based on inputs from focus group studies and based on analyses of popular health websites, literature surveys, and an empirical study of search queries. We defined the problem of classifying millions of health search queries into zero or more intent classes as a multi-label classification problem. Popular machine learning approaches for multi-label classification tasks (namely, problem transformation and algorithm adaptation methods) were not feasible due to the limitation of label data creations and health domain constraints. Another challenge in solving the search intent identification problem was mapping terms used by laymen to medical terms. To address these challenges, we developed a semantics-driven, rule-based search intent mining approach leveraging rich background knowledge encoded in Unified Medical Language System (UMLS) and a crowd sourced encyclopedia (Wikipedia). The approach can identify search intent in a disease-agnostic manner and has been evaluated on three major diseases. While users often turn to search engines to learn about health conditions, a surprising amount of health information is also shared and consumed via social media, such as public social platforms like Twitter. Although Twitter is an excellent information source, the identification of informative tweets from the deluge of tweets is the major challenge. We used a hybrid approach consisting of supervised machine learning, rule-based classifiers, and biomedical domain knowledge to facilitate the retrieval of relevant and reliable health information shared on Twitter in real time. Furthermore, we extended our search intent mining algorithm to classify health-related tweets into health categories. Finally, we performed a large-scale study to compare health search intents and features that contribute in the expression of search intent from more than 100 million search queries from smarts devices (smartphones or tablets) and personal computers (desktops or laptops). SLIDES: http://www.slideshare.net/knoesis/ashutosh-thesis
Views: 190 Knoesis Center