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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: 26645 Esri Events
Spatial Data Mining II: A Deep Dive into Space-Time Analysis
 
01:16:44
Space and time are inseparable, and integrating the temporal aspect of your data into your spatial analysis leads to powerful discoveries. This workshop will build on the cluster analysis methods discussed in Spatial Data Mining I by presenting advanced techniques for analyzing your data in the context of both space and time. We will cover space-time pattern mining techniques including aggregating your temporal data into a space-time cube, emerging hot spot analysis, local outlier analysis, best practices for visualizing your space-time cube, and strategies for interpreting and sharing your results. Come learn how to use these new techniques to get the most out of your spatiotemporal data.
Views: 9090 Esri Events
DBSCAN ( Density Based Spatial  Clustering of Application with Noise )  in Hindi | DWM | Data Mining
 
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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: 20903 Last moment tuitions
Data Analysis:  Clustering and Classification (Lec. 1, part 1)
 
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Supervised and unsupervised learning algorithms
Views: 66893 Nathan Kutz
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: 4881 Esri Events
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: 2113 GRIETCSEPROJECTS
How data mining works
 
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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: 514 Technology mart
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: 47592 Domino Data Lab
Visualization of Spatial Data (COM)
 
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Subject:Computer Science Paper:Visualization techniques
Views: 281 Vidya-mitra
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.
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: 3388 FOSS4G NA
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: 4986 Enthought
Introduction to data mining and architecture  in hindi
 
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#datamining #datawarehouse #lastmomenttuitions 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://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [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: 211959 Last moment tuitions
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: 53685 Well Academy
What and where? - Machine learning for geospatial image analysis - Mathilde Ørstavik
 
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The revolution within machine learning has given rise to new, real-life, applications, especially within computer vision. This talk will focus on not just how these new machine learning techniques work, but how they can be exploited for geospatial purposes. When analyzing georeferenced images, you will not only learn what the images are depicting but you can also derive where in the world the recognized objects exist. This gives a whole new meaning to the extracted data. At Norkart we have combined our knowledge of geographical data with the newest machine learning techniques. We have tested to what extent we can extract geographical information from aerial and satellite imagery by using deep learning.Several convolutional neural networks for segmentation have been implemented and tested on multispectral geospatial images as well as traditional RGB images to improve the accuracy further. Is it possible to extract sufficiently accurate building data in order to automatically calculate the effect of solar panels? Can you discover buildings that are built illegally? Can you extract information to helps risk analysis for insurance purposes? These are questions that will be discussed further, based on our results. NDC Conferences https://ndcoslo.com https://ndcconferences.com
Views: 402 NDC Conferences
DBSCAN - Density Based Clustering Method - Full technique with visual examples
 
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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: 17523 Machine Learning - CTW
What is Data Mining?
 
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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: 19 Pedro Puerto
Basic Data Mining
 
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A Guide to ShareScope's Data Mining (stock-screening) facility
Views: 2430 ShareScope | SharePad
Hanna Meyer: "Machine-learning based modelling of spatial and spatio-temporal data" (practical)
 
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This practical session will base on the introductory lecture on machine-learning based modelling of spatial and spatio-temporal data held on Monday. Two examples will be provided to dive into machine learning for spatial and spatio-temporal data in R: The first example is a classic remote sensing example dealing with land cover classification at the example of the Banks Peninsula in New Zealand that suffers from spread of the invasive gorse. In this example we will use the random forest classifier via the caret package to learn the relationships between spectral satellite information and provided reference data on the land cover classes. Spatial predictions will then be made to create a map of land use/cover based on the trained model. As second example, the vignette "Introduction to CAST" is taken from the CAST package. In this example the aim is to model soil moisture in a spatio-temporal way for the cookfarm (http://gsif.r-forge.r-project.org/cookfarm.html). In this example we focus on the differences between different cross-validation strategies for error assessment of spatio-temporal prediction models as well as on the need of a careful selection of predictor variables to avoid overfitting. Slides: https://github.com/HannaMeyer/Geostat2018/tree/master/slides Exercise A: https://github.com/HannaMeyer/Geostat2018/tree/master/practice/LUCmodelling.html Exercise B: https://github.com/HannaMeyer/Geostat2018/tree/master/practice/CAST-intro.html Data for Exercise A: https://github.com/HannaMeyer/Geostat2018/tree/master/practice/data/
Machine Learning Tutorial 10 - Binning Data
 
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Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics). Machine Learning and Predictive Analytics. #MachineLearning Features are the term used for the columns in the analytics base table (ABT). There is a particular type of feature known as a continuous feature. These are features that have a very high cardinality because the allowed values (domain) is on a spectrum. We can convert these continuous features to categorical features through a process called binning. This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://bit.ly/JoinCCNewsletter Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 4961 Caleb Curry
Hanna Meyer: "Machine-learning based modelling of spatial and spatio-temporal data"
 
53:25
Remote sensing is a key method in bridging the gap between local observations and spatially comprehensive estimates of environmental variables. For such spatial or spatio-temporal predictions, machine learning algorithms have shown to be a promising tool to identify nonlinear patterns between locally measured and remotely sensed variables. While easy access to user-friendly machine learning libraries fosters their use in environmental sciences, the application of these methods is far from trivial. This holds especially true for spatio-temporal since its dependencies in space and time bear the risk of overfitting and considerable misinterpretation of the model performance. In this introductory lecture I will introduce the idea of using machine-learning for the (remote sensing based) monitoring of the environment and how they can be applied in R via the caret package. In this context error assessment is a crucial topic and I will show the importance of "target-oriented" spatial cross-validation strategies when working with spatio-temporal data to avoid an overoptimistic view on model performances. As spatio-temporal machine-learning models are highly prone to overfitting caused by misleading predictor variables, I will introduce a forward feature selection method that works in conjunction with target-oriented cross-validation from the CAST package. In summary this talk aims at showing how "basic" spatial machine-learning tasks can be performed in R, but also what needs to be considered for more complex spatio-temporal prediction tasks in order to produce scientifically valuable results. Based on this talk, we will go into a practical session on Tuesday, where machine-learning algorithms will be applied to two different spatial and spatio-temporal prediction tasks. Slides: https://github.com/HannaMeyer/Geostat2018/tree/master/slides
Decision Tree with Solved Example in English | DWM | ML | BDA
 
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Take the Full Course of Artificial Intelligence What we Provide 1) 28 Videos (Index is given down) 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Artificial Intelligence Sample Notes : https://goo.gl/aZtqjh To buy the course click https://goo.gl/H5QdDU if you have any query related to buying the course feel free to email us : [email protected] Other free Courses Available : Python : https://goo.gl/2gftZ3 SQL : https://goo.gl/VXR5GX Arduino : https://goo.gl/fG5eqk Raspberry pie : https://goo.gl/1XMPxt Artificial Intelligence Index 1)Agent and Peas Description 2)Types of agent 3)Learning Agent 4)Breadth first search 5)Depth first search 6)Iterative depth first search 7)Hill climbing 8)Min max 9)Alpha beta pruning 10)A* sums 11)Genetic Algorithm 12)Genetic Algorithm MAXONE Example 13)Propsotional Logic 14)PL to CNF basics 15) First order logic solved Example 16)Resolution tree sum part 1 17)Resolution tree Sum part 2 18)Decision tree( ID3) 19)Expert system 20) WUMPUS World 21)Natural Language Processing 22) Bayesian belief Network toothache and Cavity sum 23) Supervised and Unsupervised Learning 24) Hill Climbing Algorithm 26) Heuristic Function (Block world + 8 puzzle ) 27) Partial Order Planing 28) GBFS Solved Example
Views: 224615 Last moment tuitions
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: 52369 John Paul
Introduction to Data Mining: Data Aggregation
 
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In this Data Mining Fundamentals tutorial, we discuss our first data cleaning strategy, data aggregation. Aggregation is combining two or more attributes (or objects) into a single attribute (or object). -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M6V0 See what our past attendees are saying here: https://hubs.ly/H0f8Ln80 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 9335 Data Science Dojo
Introduction to Spatial Data Analysis with Python
 
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by Jenny Palomino Attendees will learn about geoprocessing, analyzing and visualizing spatial data using Python and how it compares to other available options such as desktop GIS options (ArcMap or QGIS) or R. The talk will introduce various Python projects such as PySAL, GeoPandas, and Rasterio, and give attendees a starting place for independently exploring and learning geoprocessing skills using Python.
Views: 16186 Andrea Ross
DBSCAN | Density based clustering Algorithm - Simplest Explanation  in Hindi
 
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SImplest Video about density based algorithm - DBSCAN
Views: 36509 Red Apple Tutorials
Discretizing
 
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This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501
Views: 11156 Udacity
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 69691 edureka!
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: 8067 Graphenreiter
DBSCAN Algorithm : Density Based Spatial Clustering of Applications With Noise | Data Science-ExcelR
 
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ExcelR: In this video, we will learn about, DBSCAN is a well-known data clustering algorithm that is commonly used in data.T he DBSCAN algorithm basically requires 2 parameters. Things you will learn in this video 1)What is density based clustering algorithm (DBSCAN) 2)How to determine EPS? 3)What is the core point? 4)What is a border point? 5)What is noise point? To buy eLearning course on Data Science click here https://goo.gl/oMiQMw To register for classroom training click here https://goo.gl/UyU2ve To Enroll for virtual online training click here " https://goo.gl/JTkWXo" SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For K-Means Clustering Tutorial click here https://goo.gl/PYqXRJ For Introduction to Clustering click here Introduction to Clustering | Cluster Analysis #ExcelRSolutions #DBSCAN#Differenttypesofclusterings#EPS#corepoint#borderpoint#noisepoint#DataScienceCertification #DataSciencetutorial #DataScienceforbeginners #DataScienceTraining ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
KDD ( knowledge data discovery )  in data mining in hindi
 
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#kdd #datawarehouse #datamining #lastmomenttuitions 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://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [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: 71783 Last moment tuitions
Machine Learning #75 Density Based Clustering
 
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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: 4083 Xoviabcs
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning, definition & explanation
 
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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: 7543 The Audiopedia
Introduction to Spatial Analysis (GIS) using ArcGIS Desktop and the Time Slider Window
 
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www.knowgis.com (13.2 Minutes) Visit www.KnowGIS.com for more free tutorials. In this free tutorial, we animate spatial features over time, using the ArcGIS Time Slider Window. During this tutorial, we use ArcGIS Destop 10, talk of Spatial Analysis as a method of analysing data though a process of applying analytical techniques. provides an example of how it could be used to facilitate visual analysis. As part of this tutorial, we will "time-enable" spatial data relating to water wells mapped for the state of Montana, USA. We will visualize how data changes over time which provides opportunities for in-depth visual analysis. After a short discussion on Spatial Analysis, we will set the foundation for using the Time Slider Window so we can view temporal change though an animation. The animation will then be exported to a movie for use in non-linear video editing. The Time Slider window provides unique controls that allow spatial data to be visualized in an "animated map". The Time Slider window is invoked by clicking the Open Time Slider Window button on the Tools toolbar. Keep in mind that this button may be unavailable if you do not have any time-enabled datasets in your map, scene, or ArcGIS globe. We;ll add a temporal dataset to ArcMap and then set its time properties in order to visualize it through time using the Time Slider in ArcMap, ArcGlobe, or ArcScene. Visit www.knowgis.com for more tutorials or to learn more about the complete training series for learning and knowing ArcGIS Desktop. Thank you.
Views: 33297 Jere Folgert
Esri 2014 UC Tech Session: Spatial Data Mining: A Deep Dive into Cluster Analysis
 
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Technical workshop conducted by Lauren Bennett and Flora Vale at the 2014 user conference in San Diego.
Views: 676 Esri Events
Spatial Data Clustering Using Neighborhood Approach (NSA)
 
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Spatial Data Clustering Using Neighborhood Approach (NSA)
Views: 93 Nandkishor Dhawale
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: 104589 Bharatendra Rai
K Means Clustering Data Mining Example | Machine Learning part 1
 
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K-means clustering algorithm is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. The problem is computationally difficult (NP-hard); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both k-means and Gaussian mixture modeling. Additionally, they both use cluster centers to model the data; however, kmeans clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes. ====================================================== watch part 2 here: https://www.youtube.com/watch?v=AukQSbtZ1NQ book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar
Views: 21396 fun 2 code
INTRODUCTION TO TEXT MINING IN HINDI
 
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find relevant notes at-https://viden.io/
Views: 8686 LearnEveryone
Data Mining & Business Intelligence | Tutorial #11 | Smoothing by Binning (Solved Problem)
 
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Order my books at 👉 http://www.tek97.com/ #RanjiRaj #DataMining #Binning Worried about how to smooth data by binning with means, medians and mode well this video will solve your queries. Watch now ! قلق حول كيفية بسلاسة البيانات عن طريق binning بالوسائل ، والوسائط والوضع بشكل جيد هذا الفيديو سوف يحل استفساراتك. شاهد الآن ! Besorgt darüber, wie Daten durch Binning mit Mitteln, Medianen und Modus geglättet werden können, wird dieses Video Ihre Fragen lösen. Schau jetzt ! Preocupado acerca de cómo suavizar los datos al agrupar los medios, las medianas y el modo, este video resolverá sus dudas. Ver ahora ! Inquiet sur la façon de lisser les données en binning avec les moyens, les médianes et le mode bien cette vidéo va résoudre vos questions. Regarde maintenant ! Беспокоитесь о том, как сгладить данные путем биннинга средствами, медианами и режимами, это видео решает ваши запросы. Смотри ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 7185 Ranji Raj
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: 456517 Brandon Weinberg
Temporal analysis: Generating time series from events based data
 
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Often data is captured in a different format than required for analysis. Have you ever needed to perform historical analysis on events-based data? For example, how do you calculate turnover based on employees' start and end dates? Or, if sensor data captures when a device switches between on, off, and idle, how do you calculate the percent of time that a device was active per period? Join this Jedi session to find out!
Views: 659 Tableau Software
MATLAB Tools for Scientists: Introduction to Statistical Analysis
 
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Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe ------------------------------------------------------------------------- Researchers and scientists have to commonly process, visualize and analyze large amounts of data to extract patterns, identify trends and relationships between variables, prove hypothesis, etc. A variety of statistical techniques are used in this data mining and analysis process. Using a realistic data from a clinical study, we will provide an overview of the statistical analysis and visualization capabilities in the MATLAB product family. Highlights include: • Data management and organization • Data filtering and visualization • Descriptive statistics • Hypothesis testing and ANOVA • Regression analysis
Views: 17252 MATLAB
Spatial Statistics Tools in ArcGIS
 
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Understand hidden spatial relationships and patterns in your data using ArcGIS. Use spatial statistics and analysis to view clusters and hotspots. Watch this video to see a demo of these tools in action, and see an example of how to find and solve a real-world problem. The case study in this video examines childhood obesity rates to identify potential causes and possible solutions. Learn more: http://www.esri.com/arcgis/about-arcgis
Views: 6270 ArcGIS
Machine Learning & Geospatial Analytics
 
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Visualise and Predict in Real Time Presented by Francesco Gianferrari Pini, Founder, Quantyca Carlo Arioli, EMEA Marketing Manager, Vertica
Views: 159 Quantyca
Density-based Clustering: DBSCAN and OPTICS by Mainak Sen | Data Science Summit 2017
 
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DBSCAN and OPTICS clustering are two of the most well-known density based clustering algorithms of machine learning & data mining. Mainak Sen, Director, Business Brio delivered an interesting presentation on DBSCAN and OPTICS clustering algorithm at 4th International Data Science Summit.