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Web Usage Mining
 
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Clustering of the web users based on the user navigation patterns....
Views: 6871 GRIETCSEPROJECTS
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: 47490 Well Academy
A SURVEY ON WEB USAGE MINING TECHNIQUES
 
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International conference on E-commerce and Information Technology 2013 22-23 July 1013, Grand Oriental Hotel Colombo, Sri Lanka by - Mr. Abdul Rahaman wahab sait Lecturer, Shaqra University, Kingdom of Saudi Arabia (Research Scholar, Alagappa University,India)
Views: 921 ICRD Sri Lanka
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: 344 The Audiopedia
web usage mining research papers 2011
 
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Visit Our Website: https://goo.gl/TIo1T2?28463
Final Year Projects | Web usage mining to improve the design of an e-commerce website
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 329 Clickmyproject
Web Mining Complete Introduction ( with Definition and it's type)
 
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CLICK TO GET COMPLETE COURSE :- https://gradesetter.com/ In this web data mining / web mining video i am going to discuss with you about data mining web or data mining websites with web content mining and web usage mining and also about web mining tools ?.data mining companies
Final Year Projects | Web usage mining to improve the design of an e-commerce
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 1895 myproject bazaar
Mining Your Logs - Gaining Insight Through Visualization
 
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Google Tech Talk (more info below) March 30, 2011 Presented by Raffael Marty. ABSTRACT In this two part presentation we will explore log analysis and log visualization. We will have a look at the history of log analysis; where log analysis stands today, what tools are available to process logs, what is working today, and more importantly, what is not working in log analysis. What will the future bring? Do our current approaches hold up under future requirements? We will discuss a number of issues and will try to figure out how we can address them. By looking at various log analysis challenges, we will explore how visualization can help address a number of them; keeping in mind that log visualization is not just a science, but also an art. We will apply a security lens to look at a number of use-cases in the area of security visualization. From there we will discuss what else is needed in the area of visualization, where the challenges lie, and where we should continue putting our research and development efforts. Speaker Info: Raffael Marty is COO and co-founder of Loggly Inc., a San Francisco based SaaS company, providing a logging as a service platform. Raffy is an expert and author in the areas of data analysis and visualization. His interests span anything related to information security, big data analysis, and information visualization. Previously, he has held various positions in the SIEM and log management space at companies such as Splunk, ArcSight, IBM research, and PriceWaterhouse Coopers. Nowadays, he is frequently consulted as an industry expert in all aspects of log analysis and data visualization. As the co-founder of Loggly, Raffy spends a lot of time re-inventing the logging space and - when not surfing the California waves - he can be found teaching classes and giving lectures at conferences around the world. http://about.me/raffy
Views: 25170 GoogleTechTalks
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: 44 KDD2017 video
web mining
 
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Views: 79 Hama Awat
What is Web Mining
 
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Views: 12982 TechGig
40 Data Analysis New Tools - analyticip.com
 
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Views: 86 Data Analytics
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
SEO - Keyword discovery tool - Mozenda Data Mining - analyticip.com
 
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Views: 72 Data Analytics
Web Mining SQIT3033
 
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None-- Created using PowToon -- Free sign up at http://www.powtoon.com/ . Make your own animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 5489 Jason Ong
Web Mining: Methods and Tools, Elad Segev
 
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Web Mining: Methods and Tools, a lecture by Elad Segev. The lecture was given during the Scholarly use of Web archives: Studying Israeli Politics on the Web,The Fifth Annual Conference of the Israeli Forum for Internet and Technology Researchers held at BIU in May 2013. For All Videos: http://www.youtube.com/playlist?list=PLXF_IJaFk-9DheU5AKzYO5fgCQFFLbAp9 Bar-Ilan University: http://www1.biu.ac.il/en
Views: 3847 barilanuniversity
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: 116 Juan Carlos Ucles
Introduction to WebMining - Part 1
 
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Introduction to Web Mining and its usage in E-Commerce Websites. This is part 1. This will contain introduction of the field and in part two we will discuss its usage in E-Commerce website. Please don't forget to give your feedback... :)
Views: 4449 zdev log
Associative Classification ll Classification Using Frequent Patterns Explained in Hindi
 
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Views: 4195 5 Minutes Engineering
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: 108 The Audiopedia
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: 372920 Thales Sehn Körting
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: 81 Data Analytics
What is machine learning and how to learn it ?
 
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http://www.LearnCodeOnline.in Machine learning is just to give trained data to a program and get better result for complex problems. It is very close to data mining. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. Fraud detection? One of the more obvious, important uses in our world today. fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com
Views: 686118 Hitesh Choudhary
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: 23203 Blockgeeks
web content mining
 
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-- Created using PowToon -- Free sign up at http://www.powtoon.com/youtube/ -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 1785 vijeta kamal
Ranking Web Pages - CS101 - Udacity
 
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Other 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 Q&A: http://www.youtube.com/playlist?list=PLDA5F9F71AFF4B69E To gain access to interactive quizzes, homework, programming assignments and a helpful community, join the class at http://www.udacity.com
Views: 1421 Udacity
Web Mining - 01
 
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Views: 219 Sarbast Tube
What is Spatial Data - An Introduction to Spatial Data and its Applications
 
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Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com Spatial Data, also referred to as geospatial data, is the information that identifies the geographic location of physical objects on Earth. It’s data that can be mapped, as it is stored as coordinates and topology. In this video, we introduce the concept of Spatial Data and break down the fundamentals of interacting with Spatial Data using common development tools. We then explore how these basics can be expanded upon in modern applications to assist in daily tasks, perform detailed analyses, or create interactive user experiences. Watch this video to learn: - What is Spatial Data - How and when to use Spatial Data - Spatial Data Examples and real-world applications
Views: 5868 Fullstack Academy
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
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: 243 CyberScrap youpul
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: 487 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: 384784 YouTube NJIT
Web Mining
 
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Web Mining
Views: 289 Blind Bakhtyar
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 434651 Brandon Weinberg
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] Call us at US: +18336900808 (Toll Free) or India: +918861301699 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: 16336 edureka!
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: 118 Magalyn Melgarejo
Simulation of Data Mining Algorithms | Cloud Technologies | IEEE Projects Hyderabad | Ameerpet
 
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Simulation of Data Mining Algorithms Cloud technologies is one of the best renowned software development company In Hyderabad India. We guide and train the students based on their qualification under the guidance of vast experienced real time developers.
Views: 53 Cloud Technologies
text mining, web mining and sentiment analysis
 
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text mining, web mining
Views: 1499 Kakoli Bandyopadhyay
Web Usage Mining aplicado a LMS 2do Evento Internacional 2012
 
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Segundo Evento Internacional de Educación y TIC's (Elearning) 2012, organizado por la Unidad de Educación Virtual CEC-EPN Tema: Web Usage Mining aplicado a LMS Expositor: Julian Monsalve -- Colombia Fecha: Noviembre de 2012
Views: 75 virtualepn
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: 296 The Audiopedia
Web usage and content mining for modelling the users of the Bidasoa Turismo website
 
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Web usage and content mining to extract knowledge for modelling the users of the Bidasoa Turismo website and to adapt it. Full article available on ScienceDirect: http://dx.doi.org/10.1016/j.eswa.2013.07.040
Views: 369 Elsevier Journals
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
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: 1836 The Audiopedia

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