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Spatial Data Mining I: Essentials of Cluster Analysis
 
01:07:14
Whenever we look at a map, it is natural for us to organize, group, differentiate, and cluster what we see to help us make better sense of it. This session will explore the powerful Spatial Statistics techniques designed to do just that: Hot Spot Analysis and Cluster and Outlier Analysis. We will demonstrate how these techniques work and how they can be used to identify significant patterns in our data. We will explore the different questions that each tool can answer, best practices for running the tools, and strategies for interpreting and sharing results. This comprehensive introduction to cluster analysis will prepare you with the knowledge necessary to turn your spatial data into useful information for better decision making.
Views: 19608 Esri Events
Machine Learning on Geospatial Datasets for Segmentation, Prediction and Modeling
 
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by Stuart Lynn Machine learning is powerful technique that allows us to create predictive data driven models that can learn off complex multivariable data. The geospatial world is full of such datasets where its hard to know exactly how the input variables to your model will effect the outcomes. There exists a growing ecosystem of libraries and frameworks like Tensor Flow and Scikit-Learn that allow for sophisticated machine learning to take place but very few are easily interoperable with geospatial frameworks like PostgreSQL.. In this talk I will discuss ongoing work at CartoDB to integrate machine learning as a key analysis tool for geospatial data. Focusing on our work using random forests, neural networks and Markov chains I will talk about how these methods need to be adapted to work with geospatial data, how we can use the PL/Python extension in PostgreSQL to bring the power of these models to our geospatial data sets and discuss kinds of new analysis these methods open up In particular I will discuss about our work to develop segmentation models that are able to take a set of example observations and train a predictive model based underlying multivariate geospatial datasets like the census and use this model to predict new observations in regions where the original data was missing..
Views: 3075 FOSS4G NA
Machine Learning in ArcGIS
 
01:01:30
Machine Learning (ML) refers to a set of data-driven algorithms and techniques that automate the prediction, classification, and clustering of data. Machine learning can play a critical role in spatial problem solving in a wide range of application areas, from image classification to spatial pattern detection to multivariate prediction. In addition to traditional Machine Learning techniques, ArcGIS also has a subset of ML techniques that are inherently spatial. This workshop will cover the wide range of both traditional and spatial ML tools currently in ArcGIS, how to integrate built-in tools with other machine learning packages (from scikit-learn and TensorFlow in Python to caret in R to IBM Watson and Microsoft AI), and give you a glimpse at the road ahead.
Views: 2157 Esri Events
DBSCAN ( Density Based Spatial  Clustering of Application with Noise )  in Hindi | DWM | Data Mining
 
03:22
Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 10686 Last moment tuitions
Data Analysis:  Clustering and Classification (Lec. 1, part 1)
 
26:59
Supervised and unsupervised learning algorithms
Views: 58838 Nathan Kutz
A.I. Experiments: Visualizing High-Dimensional Space
 
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Check out https://g.co/aiexperiments to learn more. This experiment helps visualize what’s happening in machine learning. It allows coders to see and explore their high-dimensional data. The goal is to eventually make this an open-source tool within TensorFlow, so that any coder can use these visualization techniques to explore their data. http://g.co/aiexperiments Built by Daniel Smilkov, Fernanda Viégas, Martin Wattenberg, and the Big Picture team at Google. More resources: http://www.tensorflow.org
Views: 457196 Google Developers
Week 1: Spatial Data, Spatial Analysis, Spatial Data Science
 
01:15:56
Recorded lecture by Luc Anselin at the University of Chicago (September 2017).
Views: 4709 GeoDa Software
Getting Started with Spatial Data Analysis in R
 
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Spatial and spatial-temporal data have become pervasive nowadays. We are constantly generating spatial data from route planners, sensors, mobile devices, and computers in different fields like Transportation, Agriculture, Social Media. These data need to be analyzed to generate hidden insights that can improve business processes, help fight crime in cities, and much more. Simply creating static maps from these data is not enough. In this webinar we shall look at techniques of importing and exporting spatial data into R; understanding the foundation classes for spatial data; manipulation of spatial data; and techniques for spatial visualization. This webinar is meant to give you introductory knowledge of spatial data analysis in R needed to understand more complex spatial data modeling techniques. In this webinar, we will cover the following topics: -Why use R for spatial analysis -Packages for spatial data analysis -Types of spatial data -Classes and methods in R for spatial data analysis -Importing and exporting spatial data -Visualizing spatial data in R
Views: 42639 Domino Data Lab
Deep Learning with Geospatial Data | SciPy 2017 | Shane Grigsby
 
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Deep learning is all the rage these days, but what do you do when your data isn’t handwritten digits, or pictures of cats? Geospatial data comes with it’s own unique challenges—huge high dimensional datasets in weird file formats, irregular and often mismatched grids, and a pervasive lack of labeled training data… to name just a few! In this talk, we’ll explore cutting up and resampling giant remote sensing rasters using modern python tools like rasterio, georasters, and GDAL; detrending, extracting, and storing information with pandas; sensible dataset and dimensionality reduction through scipy transforms; and a few places to go in Keras and other deep learning libraries. Come learn how to jump from satellite or airborne data to your own “geoMINST” database that’s ingestible to your favorite deep learning technique!
Views: 4236 Enthought
Predictive Analytics & Machine Learning with SAP HANA
 
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Predictive Analytics & Machine Learning with SAP HANA combines the depth and speed of in-memory analytics with the power of native predictive algorithms. Together with SAP Predictive Analysis for visualization, R's extensive library of statistical and data mining techniques, and the SAP HANA predictive analytic library, you get everything you need to predict the future -- in real-time.
Views: 55986 SAP Technology
Esri 2014 UC Tech Session: Spatial Data Mining: A Deep Dive into Cluster Analysis
 
01:15:45
Technical workshop conducted by Lauren Bennett and Flora Vale at the 2014 user conference in San Diego.
Views: 561 Esri Events
data mining technology
 
01:07
Make an animated explainer video for free at: http://www.rawshorts.com Now you create your own explainer videos and animated presentations for free. Raw Shorts is a free cloud based video builder that allows you to make awesome explanation videos for your business, website, startup video, pitch video, product launch, video resume, landing page video or anything else you could use an animated explainer video. Our free video templates and explainer video software will help you create presentation videos in an instant! It's never been easier to make an animated explainer video with outstanding production value and without the cost or hassle of hiring an expensive production company or animation studio. Wait no more! Our animation software is free to use. You can make an animated video today for your landing page, website, kickstarter video, indiegogo video, pitch video and more. Simply log on and select from thousands of animated icons, animated characters and free video templates for business to make the perfect web video for your business.
Views: 466 jojo20
Advanced Machine Learning  Methods for Remote Sensing Data (Manifold) Part 1
 
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1) BIG data analyzing methods: It has now been recognized that mining for information and knowledge from large databases and documents will be the next revolution in database systems.We have entered the extremely large data era. 2) Mining Complex Types of Data: Advanced data mining uncover knowledge from stream, time-series, sequence, graph, image, and Multirelational data. We first examine how to perform multidimensional analysis and descriptive mining of complex data objects. Advanced machine Learning for Remote sensing data In recent years, machine learning methods play an essential role in the data analysis of remote sensing, including image classification, image segmentation, registration and fusion, target detection, information retrieval, etc. Researchers are now beginning to adapt advanced modern machine learning and pattern recognition techniques, such as manifold learning, sparse representation, low-rank presentation, compressive sensing and deep learning, to solve related problems in the complex remote sensing data. as we know the classic Remote sensing techniques and
Beyond Where: Modeling Spatial Relationships and Making Predictions
 
01:02:58
This workshop will cover regression analysis concepts for the analysis of geographic data. Using these statistical methods in many areas (e.g., business, public health, natural resources) allows you to examine, model, and explore data relationships to help answer questions such as “why do we see so much disease in particular areas?” Regression analysis also allows you to predict spatial outcomes for other places or time periods. Application and use of ordinary least squares regression (OLS) and geographically weighted regression (GWR) will be demonstrated. You will learn how to build a properly specified OLS model and interpret the results and diagnostics. The latest advancements in regression and prediction in ArcGIS will be covered.
Views: 745 Esri Events
How data mining works
 
12:20
Data mining concepts Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term "data mining" is in fact a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java[8] (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons.[9] Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.Data mining Data mining involves six common classes of tasks: Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation. Association rule learning (dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis. Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data. Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam". Regression – attempts to find a function which models the data with the least error that is, for estimating the relationships among data or datasets. Summarization – providing a more compact representation of the data set, including visualization and report generation.
Views: 275 Technology mart
Spatial Image Clustering Analysis
 
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As our main project, we propose the idea of spatial data mining, with the concept of clustering as the prime technique. In this, we cluster the spatial data sets and evaluate the results.
Views: 2079 GRIETCSEPROJECTS
Large scale geospatial data analysis through efficient supervised machine learning
 
01:11
The present thesis aims to test the viability of the integration of machine learning capabilities into web map servers. The validation of this hypothesis has been carried out by the development of a pre-operational prototype. The developed prototype is a platform for thematic mapping by supervised learning from very high resolution remote sensing imagery data through a web platform. This contribution overcomes the current state of art, characterized by the separation of the two areas, which requires a continuous involvement of remote sensing experts in thematic mapping intensive tasks: labour intensive tasks are supplemented by the integration of the scalability capabilities from machine learning engines and web map servers. With this hypothesis the application field referred to the semi-automatic creation of large scale thematic maps can open up different fields, from agriculture to the environmental monitoring field, to expert users of these applications domains with limited specific knowledge of remote sensing techniques. Semantic tagging algorithms based on supervised classification methods can be exploited for thematic map creation from raster data based on user needs. This requires the integration of machine learning capabilities within web map servers, along with a simple interface that enables navigation and the monitoring of geospatial learning. The adaptive nature of this learning, along with its integration into a web server, requires a classification algorithm characterized by efficient management and processing of data in time scales compatible with traditional web browsing. At the same time, the volume of data managed by remote sensing applications motivates the transfer of the developed methodology to cloud environments under the Big Data paradigm. Ph.D. work developed by Dr. Javier Lozano in Vicomtech-IK4 and presented at the University of the Basque Country. Directed by: Dr. Ekaitz Zulueta and Dr. Marco Quartulli. More information: tech.t[email protected]
Views: 607 Vicomtech
Athlytics: Data Mining and Machine Learning for Sports Analytics part 1
 
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Author: Evangelos Papalexakis, Department of Computer Science and Engineering, University of California, Riverside Konstantinos Pelechrinis, School of Information Sciences, University of Pittsburgh Abstract: Data and analytics have been part of the sports industry from as early as the 1870s, when the first boxscore in baseball was recorded. However, it is only recently that advanced data mining and machine learning techniques have been utilized for facilitating the operations of sports franchises. While part of the reason is related with the ability to collect more fine-grained data, an equally important factor for this turn to analytics is the huge success and competitive advantage that early adopters of investment in analytics enjoyed (popularized by the best-seller ``Moneyball'' that described the success that Oakland Athletics had with analytics). Draft selection, game-day decision making and player evaluation are just a few of the applications where sports analytics play a crucial role today. Apart from the sports clubs, other stakeholders in the industry (e.g., the leagues' offices, media, etc.) invest in analytics. The leagues increasingly rely on data in order to decide on potential rule changes. For instance, the most recent rule change in NFL, i.e., the kickoff touchback, was a result of thorough data analysis of concussion instances. In this tutorial we will review the literature in data mining and machine learning techniques for sports analytics. We will introduce the audience to the design and methodologies behind advanced metrics such as the adjusted plus/minus for evaluating basketball players, spatial metrics for evaluating the ability of a player to spread the defense in basketball, and the Player Efficiency Rating (PER). We will also go in depth in advanced data mining methods, and in particular tensor mining, that can analyze heterogenous data similar to the ones available in today's sports world. Link to tutorial: http://www.pitt.edu/~kpele/kdd2017-tutorial.html More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 232 KDD2017 video
3D Spatial Data Mining on Document Sets
 
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The retrospective fault analysis of complex technical devices based on documents emerging in the advanced steps of the product life cycle can reveal error sources and problems, which have not been discovered by simulations or other test methods in the early stages of the product life cycle. This video presents a novel approach to support the failure analysis through (i) a semi-automatic analysis of databases containing product-related documents in natural language (e.g. problem and error descriptions, repair and maintenance protocols, service bills) using information retrieval and text mining techniques and (ii) an interactive exploration of the data mining results. Our system supports visual data mining by mapping the results of analyzing failure-related documents onto corresponding 3D models. Thus, visualization of statistics about failure sources can reveal problem sources resulting from problematic spatial configurations. This video can be found in high quality at wwwisg.cs.uni-magdeburg.de/~timo/videos/3DSpDataMining.avi The associated scientific publication available at wwwisg.cs.uni-magdeburg.de/~timo/ was published at the 2nd International Conference on Computer Graphics Theory and Applications (GRAPP'07)
Views: 8066 Graphenreiter
8 Mining Complex Types of Data- Data Warehouse and Data Mining
 
01:27:25
http://www.atozsky.com/ https://www.facebook.com/atozsky.computer/ All credits goes to NIELIT, Delhi INDIA
Views: 119 AtoZ COMPUTER
Visualization of Spatial Data (COM)
 
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Subject:Computer Science Paper:Visualization techniques
Views: 176 Vidya-mitra
Machine Learning #75 Density Based Clustering
 
17:51
Machine Learning #75 Density Based Clustering Machine Learning Complete Tutorial/Lectures/Course from IIT (nptel) @ https://goo.gl/AurRXm Discrete Mathematics for Computer Science @ https://goo.gl/YJnA4B (IIT Lectures for GATE) Best Programming Courses @ https://goo.gl/MVVDXR Operating Systems Lecture/Tutorials from IIT @ https://goo.gl/GMr3if MATLAB Tutorials @ https://goo.gl/EiPgCF
Views: 2474 Xoviabcs
What is Data Mining?
 
03:25
I Have No Intention To Claim The Ownership Of This Video All Credits To The Owner Of This Video! This Has Been Upload For Educational Purpose Only. Please Do Not Take Down This Channel! If You Do Not Agree Please Message Me So That I Can Delete The Video! Thank You Very Much! Original Video Link: https://www.youtube.com/watch?v=R-sGvh6tI04 Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] It is an interdisciplinary subfield of computer science.[1][2][3] The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.[1] Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[4]The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.[5] It also is a buzzword[6] and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java[7] (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons.[8] Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations. Lets Connect: Twitter: https://twitter.com/BLAmedia1 Google+: https://plus.google.com/115816603020714793797 Facebook: https://www.facebook.com/BLAmedia-1884144591836064 LinkedIn: https://www.linkedin.com/in/blamedia
Views: 16 Pedro Puerto
Current trends in Data Mining..
 
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Topic described here are: Multimedia datamining Ubiquitous datamining Distributed datamining Spatial datamining Time series datamining Text mining Video mining Image mining Audio mining multimedia issues Submitted by: A. Vaishnavi II Msc cs A 175214141
Views: 86 vaishu raj
Introduction to data mining and architecture  in hindi
 
09:51
Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 153857 Last moment tuitions
Brian Kent: Density Based Clustering in Python
 
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PyData NYC 2015 Clustering data into similar groups is a fundamental task in data science. Probability density-based clustering has several advantages over popular parametric methods like K-Means, but practical usage of density-based methods has lagged for computational reasons. I will discuss recent algorithmic advances that are making density-based clustering practical for larger datasets. Clustering data into similar groups is a fundamental task in data science applications such as exploratory data analysis, market segmentation, and outlier detection. Density-based clustering methods are based on the intuition that clusters are regions where many data points lie near each other, surrounded by regions without much data. Density-based methods typically have several important advantages over popular model-based methods like K-Means: they do not require users to know the number of clusters in advance, they recover clusters with more flexible shapes, and they automatically detect outliers. On the other hand, density-based clustering tends to be more computationally expensive than parametric methods, so density-based methods have not seen the same level of adoption by data scientists. Recent computational advances are changing this picture. I will talk about two density-based methods and how new Python implementations are making them more useful for larger datasets. DBSCAN is by far the most popular density-based clustering method. A new implementation in Dato's GraphLab Create machine learning package dramatically speeds up DBSCAN computation by taking advantage of GraphLab Create's multi-threaded architecture and using an algorithm based on the connected components of a similarity graph. The density Level Set Tree is a method first proposed theoretically by Chaudhuri and Dasgupta in 2010 as a way to represent a probability density function hierarchically, enabling users to use all density levels simultaneous, rather than choosing a specific level as with DBSCAN. The Python package DeBaCl implements a modification of this method and a tool for interactively visualizing the cluster hierarchy. Slides available here: https://speakerdeck.com/papayawarrior/density-based-clustering-in-python Notebooks: http://nbviewer.ipython.org/github/papayawarrior/public_talks/blob/master/pydata_nyc_dbscan.ipynb http://nbviewer.ipython.org/github/papayawarrior/public_talks/blob/master/pydata_nyc_DeBaCl.ipynb
Views: 12354 PyData
What is Data Mining
 
08:10
Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a buzzword, and is frequently misused to mean any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) but is also generalized to any kind of computer decision support system, including artificial intelligence, machine learning, and business intelligence. In the proper use of the word, the key term is discovery[citation needed], commonly defined as "detecting something new". Even the popular book "Data mining: Practical machine learning tools and techniques with Java"(which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting are part of the data mining step, but do belong to the overall KDD process as additional steps.
Views: 51887 John Paul
Artificial Intelligence and Machine Learning with ArcGIS
 
11:47
Navigate the world of ArcGIS, Artificial Intelligence (AI) and Machine Learning (ML), and how ArcGIS supports integration with open frameworks to solve spatial problems. See More 2018 Esri Federal GIS Plenary - http://p.ctx.ly/r/745v --------------------------------------------------------------------------------------------------------------------------  Follow us on Social Media! Twitter: https://twitter.com/Esri Facebook: https://facebook.com/EsriGIS LinkedIn: https://www.linkedin.com/company/esri Instagram: https://www.instagram.com/esrigram     The Science of Where  http://www.esri.com
Views: 8266 Esri Events
DBSCAN - Density Based Clustering Method - Full technique with visual examples
 
12:50
Here we discuss DBSCAN which is one of the method that uses Density based clustering method. Here we discuss the Algorithm, shows some examples and also give advantages and disadvantages of DBSCAN. The url of dbscan in python : http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html
Views: 11594 Machine Learning - CTW
Basics of Data Mining
 
09:50
Views: 57174 Prabhudev Konana
Introduction to Cluster Analysis with R - an Example
 
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Provides illustration of doing cluster analysis with R. R File: https://goo.gl/BTZ9j7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - Illustrates the process using utilities data - data normalization - hierarchical clustering using dendrogram - use of complete and average linkage - calculation of euclidean distance - silhouette plot - scree plot - nonhierarchical k-means clustering Cluster analysis is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 91915 Bharatendra Rai
DBSCAN | Density based clustering Algorithm - Simplest Explanation  in Hindi
 
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SImplest Video about density based algorithm - DBSCAN
Views: 26864 Red Apple Tutorials
Data Mining  Association Rule - Basic Concepts
 
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short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Data Mining, Classification, Clustering, Association Rules, Regression, Deviation
 
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Complete set of Video Lessons and Notes available only at http://www.studyyaar.com/index.php/module/20-data-warehousing-and-mining Data Mining, Classification, Clustering, Association Rules, Sequential Pattern Discovery, Regression, Deviation http://www.studyyaar.com/index.php/module-video/watch/53-data-mining
Views: 82546 StudyYaar.com
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning, definition & explanation
 
03:43
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning - DATA MINING definition - DATA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
Views: 5722 The Audiopedia
Spark Demo for Spatial Data
 
52:49
This video contains a detailed demonstration of how to develop a spatial Spark application with python, docker, docker-compose and docker swarm mode. Furthermore, we are deploying the system to AWS via docker-machine just within minutes. A web page containing detailed explanations and links to resources is on github: https://github.com/mwernerds/big_geospatial_data_lecture/tree/master/06_spark_demo
Views: 192 Martin Werner
Bruno Goncalves, Anastasios Noulas: Mining Georeferenced Data
 
01:28:22
PyData NYC 2015 The democratization of GPS enabled devices has led to a surge of interest in the availability of high quality geocoded datasets. This data poses both opportunities and challenges for the study of social behavior. The goal of this tutorial is to introduce its attendants to the state-of-the-art in the mining and analysis in this new world of spatial data with a special focus on the real world. In this tutorial we will provide an overview of workflows for location rich data, from data collection to analysis and visualization using Python tools. In particular: Introduction to location rich data: In this part tutorial attendees will be provided with an overview perspective on location-based technologies, datasets, applications and services Online Data Collection: A brief introductions to the APIs of Twitter, Foursquare, Uber and AirBnB using Python (using urllib2, requests, BeautifulSoup). The focus will be on highlighting their similarities and differences and how they provide different perspectives on user behavior and urban activity. A special reference will be provided on the availability of Open Datasets with a notable example being the NYC Yellow Taxi dataset (NYC Taxy) Data analysis and Measurement: Using data collected using the APIs listed above we will perform several simple analyses to illustrate not only different techniques and libraries (geopy, shapely, data science toolkit, etc) but also the different kinds of insights that are possible to obtain using this kind of data, particularly on the study of population demographics, human mobility, urban activity and neighborhood modeling as well as spatial economics. Applied Data Mining and Machine Learning: In this part of the tutorial we will focus on exploiting the datasets collected in the previous part to solve interesting real world problems. After a brief introduction on python’s machine learning library, scikit-learn, we will formulate three optimization problems: i) predict the best area in New York City for opening a Starbucks using Foursquare check-in data, ii) predict the price of an Airbnb listing and iii) predict the average Uber surge multiplier of an area in New York City. Visualization: Finally, we introduce some simple techniques for mapping location data and placing it in a geographical context using matplotlib Basemap and py.processing. Slides available here: http://www.slideshare.net/bgoncalves/mining-georeferenced-data Code here: https://github.com/bmtgoncalves/Mining-Georeferenced-Data
Views: 1179 PyData
Datamining in Science: Mining Patterns in Protein StructuresΓÇöAlgorithms and Applications
 
01:19:18
With the data explosion occurring in sciences, utilizing tools to help analyze the data efficiently is becoming increasingly important. This session will describe tools included with SQL Server (Yukon), and Wei Wang will describe the MotifSpace projectΓÇöa comprehensive database of candidate spatial protein motifs based on recently developed data mining algorithms. One of the next great frontiers in molecular biology is to understand and predict protein function. Proteins are simple linear chains of polymerized amino acids (residues) whose biological functions are determined by the three-dimensional shapes that they fold into. A popular approach to understanding proteins is to break them down into structural sub-components called motifs. Motifs are recurring structural and spatial units that are frequently correlated with specific protein functions. Traditionally, the discovery of motifs has been a laborious task of scientific exploration. In this talk, I will discuss recent data-mining algorithms that we have developed for automatically identifying potential spatial motifs. Our methods automatically find frequently occurring substructures within graph-based representations of proteins. The complexity of protein structures and corresponding graphs poses significant computational challenges. The kernel of our approach is an efficient subgraph-mining algorithm that detects all (maximal) frequent subgraphs from a graph database with a user-specified minimal frequency.
Views: 100 Microsoft Research
Adventures in Spatio-Temporal Machine Learning by Will Groves (Univ. of Minnesota)
 
01:06:47
Suppose you had a large corpus of taxi data. With just the spatio-temporal data stream (GPS location and time) from 10,000 individual taxis collected for a two week period, Will Groves will explore "big data" machine learning techniques to: predict the most likely future paths of an in-progress taxi trip, and determine characteristic patterns among the population of taxis. In this talk, Will discusses his published research in taking a real-world dataset of 1-minute resolution taxi location data for a major world metropolis to build efficient predictions and to discover patterns. These methods depart from other work in short-term trajectory prediction by using only the spatio-temoral data stream: the road network used for prediction and is emergent from the data. This makes the proposed approaches widely applicable to a variety of spatio-temporal domains where no map is known or the spatial network is changing rapidly.
Views: 342 Milibo
DATA MINING   1 Data Visualization   3 2 2  Multidimensional Scaling
 
06:49
https://www.coursera.org/learn/datavisualization
Views: 9150 Ryo Eng
What Is DATA MINING? DATA MINING Definition & Meaning
 
03:43
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning - DATA MINING definition - DATA MINING explanation. Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] Data mining is an interdisciplinary subfield of computer science with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.[1][2][3][4] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[5] Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] The term "data mining" is in fact a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.[6] It also is a buzzword[7] and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java[8] (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons.[9] Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations. Source: Wikipedia.org
Views: 13 Audiopedia
Data Analytics Webinar Series: Manipulating Large Spatial Datasets with Free & Open Source Tools
 
54:52
Large data sets often present unique challenges. Processes may fail or take long to complete. This session will cover how to use the free R package to manipulate large geographic data sets as a set of repeatable processes -- helping you to automate your workflow and overcome these unique challenges. Ready to improve your scripting kung fu? During this webinar, you will learn: -how to read and manipulate point datasets (geocoded locations) -how to aggregate points to polygons (such as a census geographies) -how to generate a heatmap -how to combine these steps into a reusable R script Audience: This webinar is targeted at a more technical audience and is appropriate for anyone who has some experience with command line tools or scripting. Level of Difficulty: Experienced
Views: 878 azavea
Hierarchical Clustering - Agglomerative, Divisive- Algorithm with visual examples
 
11:46
Here we discuss about Hierarchical Clustering where we briefly discuss what is Hierarchical clustering, why it is popular. Two types of hierarchical clustering - Agglomerative and Divisive, and also discuss the algorithms then we discuss what is proximity matrix and the different way to make it and the strength and limitations of them
Detecting anomalies with  Oracle Big Data Spatial and Graph
 
03:48
Detecting fraud and anomalies with Oracle Big Data Spatial and Graph Read more: 1. Oracle Big Data Spatial and Graph on Oracle.com:  https://www.oracle.com/database/big-data-spatial-and-graph 2. OTN product page (trial software downloads, documentation):  http://www.oracle.com/technetwork/database/database-technologies/bigdata-spatialandgraph 3. Blog  (technical examples and tips):   https://blogs.oracle.com/bigdataspatialgraph/ 4. Big Data Lite Virtual Machine (a free sandbox environment to get started):   http://www.oracle.com/technetwork/database/bigdata-appliance/oracle-bigdatalite-2104726.html
Oracle Data Miner/SQL Developer + R Integration via SQL Query node
 
15:46
This presentation and demo shows the integration capabilities of Oracle Data Miner/SQL Developer + Oracle R Enterprise integration.
Views: 9047 Charlie Berger

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