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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.
Last Minute Tutorials | Apriori algorithm | Association Rule Mining
 
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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 73204 Last Minute Tutorials
data mining fp growth | data mining fp growth algorithm | data mining fp tree example | fp growth
 
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In this video FP growth algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining algorithms in hindi, data mining in hindi, data mining lecture, data mining tools, data mining tutorial, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining fp growth, data mining fp growth algorithm, data mining fp tree example, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining, fp growth algorithm, fp growth algorithm example, fp growth algorithm in data mining, fp growth algorithm in data mining example, fp growth algorithm in data mining examples ppt, fp growth algorithm in data mining in hindi, fp growth algorithm in r, fp growth english, fp growth example, fp growth example in data mining, fp growth frequent itemset, fp growth in data mining, fp growth step by step, fp growth tree
Views: 126653 Well Academy
Lecture - 34 Data Mining and Knowledge Discovery
 
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Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 134228 nptelhrd
Understanding Apriori Algorithm | Apriori Algorithm Using Mahout | Edureka
 
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Watch Sample Class Recording: http://www.edureka.co/mahout?utm_source=youtube&utm_medium=referral&utm_campaign=apriori-algo Apriori is an algorithm for frequent item set mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis. This video gives you a brief insight of Apriori algorithm. Related Blogs: http://www.edureka.co/blog/introduction-to-clustering-in-mahout/?utm_source=youtube&utm_medium=referral&utm_campaign=apriori-algo http://www.edureka.co/blog/k-means-clustering/?utm_source=youtube&utm_medium=referral&utm_campaign=apriori-algo Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics related to ‘Apriori Algorithm’ have extensively been covered in our course ‘Machine Learning with Mahout’. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 14512 edureka!
A Systematic Review on Educational Data Mining | Final year Projects 2016 - 2017
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 459 Clickmyproject
Association Rule Mining in R
 
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This video is using Titanic data file that's embedded in R (see here: https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/Titanic.html). You can find both the data and the code here: https://github.com/A01203249/YouTube-Videos.git. Use git clone to clone this repo locally and use the code.
Views: 48125 Ani Aghababyan
A Survey on Trajectory Data Mining: Techniques and Applications | Final Year Projects 2016 - 2017
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 235 myproject bazaar
Last Minute Tutorials | FP Growth | Frequent Pattern Growth
 
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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 57212 Last Minute Tutorials
Best books on Data Mining
 
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Best books on Data Mining
Views: 310 Books Magazines
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcleR
 
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Data Mining Using R (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data Mining Certification Training Course Content : https://www.excelr.com/data-mining/ Introduction to Data Mining Tutorials : https://youtu.be/uNrg8ep_sEI What is Data Mining? Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights? Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics. Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly. Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies. Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning. Topics You Will Learn… Unsupervised learning: Introduction to datamining Dimension reduction techniques Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Association rules / Market Basket Analysis / Affinity Filtering Recommender Systems / Recommendation Engine / Collaborative Filtering Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc. Cluster Analysis Hierarchical clustering K-means clustering Supervised learning: Overview of machine learning / supervised learning Data exploration methods Basic classification algorithms Decision trees classifier Random Forest K-Nearest Neighbours Bayesian classifiers: Naïve Bayes and other discriminant classifiers Perceptron and Logistic regression Neural networks Advanced classification algorithms Bayesian Networks Support Vector machines Model validation and interpretation Multi class classification problem Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees) Regression analysis Tools You Will Learn… R: R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community. Mode of Trainings : E-Learning Online Training ClassRoom Training --------------------------------------------------------------------------- For More Info Contact :: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com
Data Mining Project
 
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Data Mining Project
Data Mining with Weka (1.3: Exploring datasets)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 3: Exploring datasets http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 78892 WekaMOOC
Data Mining: How You're Revealing More Than You Think
 
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Data mining recently made big news with the Cambridge Analytica scandal, but it is not just for ads and politics. It can help doctors spot fatal infections and it can even predict massacres in the Congo. Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230 https://www.theregister.co.uk/2006/08/15/beer_diapers/ https://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/ https://www.economist.com/node/15557465 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/ https://qz.com/584287/data-scientists-keep-forgetting-the-one-rule-every-researcher-should-know-by-heart/ https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853 http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/DMSuccessStories.html http://content.time.com/time/magazine/article/0,9171,2058205,00.html https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf https://www.cs.helsinki.fi/u/htoivone/pubs/advances.pdf http://cecs.louisville.edu/datamining/PDF/0471228524.pdf https://bits.blogs.nytimes.com/2012/03/28/bizarre-insights-from-big-data https://scholar.harvard.edu/files/todd_rogers/files/political_campaigns_and_big_data_0.pdf https://insights.spotify.com/us/2015/09/30/50-strangest-genre-names/ https://www.theguardian.com/news/2005/jan/12/food.foodanddrink1 https://adexchanger.com/data-exchanges/real-world-data-science-how-ebay-and-placed-put-theory-into-practice/ https://www.theverge.com/2015/9/30/9416579/spotify-discover-weekly-online-music-curation-interview http://blog.galvanize.com/spotify-discover-weekly-data-science/ Audio Source: https://freesound.org/people/makosan/sounds/135191/ Image Source: https://commons.wikimedia.org/wiki/File:Swiss_average.png
Views: 144222 SciShow
Final presentation for Data Mining Seminar-- Boshen Lyu
 
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Final presentation for Data Mining Seminar
Views: 152 Boshen Lyu
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: 448764 Brandon Weinberg
More Data Mining with Weka (1.1: Introduction)
 
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More Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 1: Introduction http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/Le602g https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 15812 WekaMOOC
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 108781 LearnEveryone
Statistical Aspects of Data Mining (Stats 202) Day 12
 
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Google Tech Talks August 7, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Google engEDU Speaker: David Mease
Views: 353 GoogleTalksArchive
FP Growth Algorithm Example for Association Rule Mining
 
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Frequent Pattern Growth algorithm is a tree based algorithm used for Association Rule Mining. It transforms the transactional database to a tree, which is used for mining frequent patterns. The frequent patterns grow as we traverse the tree deeper. It is better than apriori algorithm because database is read only once for creating FP Tree and then the tree is subsequently used to recursively create conditional FP trees to mine it. Note to viewer: if you already know FP tree creation, you can start watching this video from 20 minutes.
Views: 43871 Moh'd Shakeb Baig
Advanced Data Mining with Weka (4.1: What is distributed Weka?)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 1: What is distributed Weka? http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 1832 WekaMOOC
Advanced Data Mining with Weka (2.1: Incremental classifiers in Weka)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 1: Incremental classifiers in Weka http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3182 WekaMOOC
Data Mining with Weka (4.4: Logistic regression)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 4: Logistic regression http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 32090 WekaMOOC
Data Mining Tool: extra features
 
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Some extra features of the Data Mining Tool. Heatmaps and Gene Set Enrichment.
Views: 59 QMRIBioinf
UML Class Diagram Tutorial
 
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Learn how to make classes, attributes, and methods in this UML Class Diagram tutorial. There's also in-depth training and examples on inheritance, aggregation, and composition relationships. UML (or Unified Modeling Language) is a software engineering language that was developed to create a standard way of visualizing the design of a system. And UML Class Diagrams describe the structure of a system by showing the system’s classes and how they relate to one another. This tutorial explains several characteristics of class diagrams. Within a class, there are attributes, methods, visibility, and data types. All of these components help identify a class and explain what it does. There are also several different types of relationships that exist within UML Class Diagrams. Inheritance is when a child class (or subclass) takes on all the attributes and methods of the parent class (or superclass). Association is a very basic relationship where there's no dependency. Aggregation is a relationship where the part can exist outside the whole. And finally, Composition is when a part cannot exist outside the whole. A class would be destroyed if the class it's related to is destroyed. Further UML Class Diagram information: https://www.lucidchart.com/pages/uml-class-diagram —— Learn more and sign up: http://www.lucidchart.com Follow us: Facebook: https://www.facebook.com/lucidchart Twitter: https://twitter.com/lucidchart Instagram: https://www.instagram.com/lucidchart LinkedIn: https://www.linkedin.com/company/lucidsoftware —— Credits for Photos with Attribution Requirements Tortoise - by Niccie King - http://bit.ly/2uHaL1G Otter - by Michael Malz - http://bit.ly/2vrVoYt Slow Loris - by David Haring - http://bit.ly/2uiBWxg Creep - by Poorna Kedar - http://bit.ly/2twR4K8 Visitor Center - by McGheiver - http://bit.ly/2uip0Hq Lobby - by cursedthing - http://bit.ly/2twBWw9
Views: 747738 Lucidchart
More Data Mining with Weka (1.5: The Command Line interface)
 
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More Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 5: The Command Line interface http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/Le602g https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 9849 WekaMOOC
Meta Brown: Data Mining for Dummies #DataTalk
 
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In our weekly #DataTalk, we had a chance to talk with Meta Brown about her work in data science and her latest book: Data Mining for Dummies. You can learn more about her by going to her website: http://www.metabrown.com/ You can read a full transcription of this video by going to: http://ex.pn/metabrown You can learn about upcoming #DataTalk events and tweetchats: http://experian.com/datatalk
Views: 1531 Experian
Site Characterization Services: Step 1 Data Mining
 
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Terracon accomplishes data mining with a GIS-based platform that manages vast amounts of geospatial information from thousands of locations across the country. This provides our clients with better information right at the start of a project.
Views: 1547 Terraconconsultants
DataMining Intro Urdu
 
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Views: 66 Abdul Malik
[PURDUE MLSS] Mining Heterogeneous Information Networks by Jiawei Han (Part 1/2)
 
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Lecture notes: http://learning.stat.purdue.edu/mlss/_media/mlss/han.pdf Mining Heterogeneous Information Networks Multiple typed objects in the real world are interconnected, forming complex heterogeneous information networks. Different from some studies on social network analysis where friendship networks or web page networks form homogeneous information networks, heterogeneous information network reflect complex and structured relationships among multiple typed objects. For example, in a university network, objects of multiple types, such as students, professors, courses, departments, and multiple typed relationships, such as teach and advise are intertwined together, providing rich information. We explore methodologies on mining such structured information networks and introduce several interesting new mining methodologies, including integrated ranking and clustering, classification, role discovery, data integration, data validation, and similarity search. We show that structured information networks are informative, and link analysis on such networks becomes powerful at uncovering critical knowledge hidden in large networks. The tutorial also presents a few promising research directions on mining heterogeneous information networks. See other lectures at Purdue MLSS Playlist: http://www.youtube.com/playlist?list=PL2A65507F7D725EFB&feature=view_all
Views: 1101 Purdue University
Discovering Binding Cores in Protein-DNA Binding Using Association Rule Mining-Statistical Measures
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 19 Clickmyproject
DATA MINING EXPLAINED IN HINDI | "ITNA SARA DATA??"
 
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नमस्कार दोस्तों,आज की वीडियो में में आप सभी को DATA MINING के बारे में बताने जा रहा हूँ की आखिर DATA MINING क्या होती है और क्या ये हमारे किसी काम आती हैं या नहीं और आखिर हमारे ज़िन्दगी में इसकी कितनी जरुरत है। आशा करता हूँ आपको ये वीडियो पसंद आएगी अगर आपको वीडियो पसंद आये तो वीडियो को LIKE SHARE और चैनल को SUBSCRIBE जरूर से करे। धन्यवाद। जय हिन्द वन्दे मातरम subscribe our channel on youtube: https://www.youtube.com/channel/UCR_kAPwG59SxWRaUfzk3qoQ facebook: https://www.facebook.com/dropouttechnical/ twitter: https://twitter.com/dropoutechnical google+: https://plus.google.com/u/0/103031877017890269380 -~-~~-~~~-~~-~- Please watch: "MOTO X4 my opinion |"Best phone??"|"worth buy at 21000"🔥" https://www.youtube.com/watch?v=r54C6_667uU -~-~~-~~~-~~-~-
Views: 11380 Dropout Technical
More Data Mining with Weka (1.6: Working with big data)
 
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More Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Working with big data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/Le602g https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 10098 WekaMOOC
Excel - Weka İlişkisi
 
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Excel, Excel - Weka, Excel Weka İlişkisi, Excel Weka Eğitim Seti, Excel Weka Eğitim Serisi TAGS veri madenciliği yöntemleri, veri madenciliği nedir, microsoft excell, makro nedir, makrolarla excell dersleri, mining data, mining, datamining, what is data, data mining pdf, data mining techniques, data analysis, mineria, mineria de datos, data warehouse, data warehousing, database, data mining algorithm, data mining ppt, database mining, data mining software, big data, clustering, data mining tools, google data mining, classification, big data mining, smite, datamining smite, smite data mining, coursera, smite reddit, smite patch notes, smite wiki,0 gw2 data mining, big data analytics, bigdata, big data mining, big data, slideshare, kaggle, data scientist, hadoop,0 data mining meaning, jurnal data mining, python data mining, data mining adalah, nptel, python, data mining pdf TAGS http://kodkolik.net/ Machine Learning Group at the University of Waikato Project Software Book Publications People Related Weka 3: Data Mining Software in Java Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this. Weka is open source software issued under the GNU General Public License. We have put together several free online courses that teach machine learning and data mining using Weka. Check out the website for the courses for details on when and how to enrol. The videos for the courses are available on Youtube. Yes, it is possible to apply Weka to big data!
Advanced Data Mining with Weka (4.6: Application: Image classification)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Application: Image classification http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 7694 WekaMOOC
Data Mining Case Study meetup:  Data Mining Overview
 
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Junling Hu presents a high level overview of data mining at the "Data Mining Case Study" meetup at the HackerDojo in Mountain View, Ca on Aug 17th 2013.
Views: 1524 Stoney Vintson
Neural Network Explained -Artificial Intelligence - Hindi
 
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Neural network in ai (Artificial intelligence) Neural network is highly interconnected network of a large number of processing elements called neuron architecture motivated from brain. Neuron are interconnected to synapses which provide input from other neurons which intern provides output i.e input to other neurons. Neuron are in massive therefore they provide distributed network. Extra Tags neural networks nptel, neural networks in artificial intelligence, neural networks in hindi, neural networks and deep learning, neural networks in r, neural networks in ai, neural networks andrew ng, neural networks in python, neural networks mit, neural networks and fuzzy logic, neural networks, neural networks tutorial, neural networks and deep learning coursera, neural networks applications, neural networks api, neural networks ai, neural networks algorithm, neural networks andrej karpathy, neural networks artificial intelligence, neural networks basics, neural networks brain, neural networks backpropagation, neural networks backpropagation example, neural networks biology, neural networks by rajasekaran free download, neural networks backpropagation tutorial, neural networks blockchain, neural networks basics pdf, neural networks bias, neural networks course, neural networks car, neural networks caltech, neural networks computerphile, neural networks demystified, neural networks demo, neural networks demystified part 1 data and architecture, neural networks data mining, neural networks demystified part 1, neural networks deep learning, neural networks demystified part 3, neural networks demystified part 2, neural networks data analytics, neural networks documentary, neural networks example, neural networks explained, neural networks edureka, neural networks explained simply, neural networks explanation, neural networks evolution, neural networks eli5, neural networks explained simple, neural networks for image recognition, neural networks for dummies, neural networks for recommender systems, neural networks for machine learning youtube, neural networks geoffrey hinton, neural networks game, neural networks google, neural networks gradient, neural networks gradient descent, neural networks genetic algorithms, neural networks gesture recognition, neural networks generations, neural networks graphics, neural networks playing games, neural networks hinton, neural networks hugo larochelle, neural networks harvard, neural networks hardware implementation, neural networks how it works, neural networks handwriting recognition, neural networks human brain, neural networks how they work, neural networks hidden units, neural networks hidden layer, neural networks in data mining, neural networks in machine learning, neural networks introduction, neural networks in tamil, neural networks in c++, neural networks java, neural networks java tutorial, neural networks javascript, neural networks jmp, neural networks js, jeff heaton neural networks, introduction to neural networks for java, neural networks khan academy, neural networks knime, recurrent neural networks keras, neural networks for kids, neural networks lecture, neural networks lecture notes, neural networks learn, neural networks linear regression, neural networks logistic regression, neural networks lstm, neural networks learning algorithms, neural networks lecture videos, neural networks lottery prediction, neural networks loss, neural networks machine learning, neural networks matlab, neural networks matlab tutorial, neural networks mathematics, neural networks music, neural networks mit opencourseware, neural networks math, neural networks meaning in tamil, neural networks mit ocw, neural networks nlp, neural networks nptel videos, neural networks numericals, neural networks ng, neural networks natural language processing, backpropagation in neural networks nptel, andrew ng neural networks, neural networks ocw, neural networks on fpga, neural networks ocr, neural networks perceptron, neural networks python tutorial, neural networks ppt, neural networks ppt download, neural networks questions and answers, neural networks robot, neural networks radiology, neural networks regularization, neural networks recurrent, neural networks rapidminer, neural networks using r, neural networks stanford, neural networks siraj, neural networks spss, neural networks sigmoid function, neural networks simple, neural networks simplified, neural networks sentdex, neural networks siraj raval, neural networks stock market, neural networks simulation, neural networks training, neural networks ted, neural networks tensorflow, neural networks types, neural networks tensorflow tutorial, neural networks tutorial python, neural networks trading, neural networks tutorial youtube,tworks 1, neural networks 2016, neural networks 3blue1brown, neural networks 3d, neural networks 3d reconstruction, neural networks in 4 minutes, lecture 9 - neural networks
Views: 9183 CaelusBot
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 65998 deltaDNA
Data Mining with Weka (2.2: Training and testing)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Training and testing http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 72678 WekaMOOC
What is Linear Regression?
 
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What is Linear Regression? In this video I explain what linear regression is, why it’s used and briefly show you how to implement it in Python using scikit learn and statmodels. I also explain R squared, t-value and p-value. More information and learning resources on Linear Regression If this has been useful, then consider giving your support by buying me a coffee https://ko-fi.com/pythonprogrammer Introduction to Statistical Learning - http://bit.ly/2ELFL6Z (Free PDF of Book) #linearregression #linearregressionpython #machinelearningalgorithms
Views: 1324 Python Programmer
Panels and Clustering in R
 
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This video runs through an example script on how to estimate panel data models in R using plm(). By appeal to lm() and lmer(), I show that plm() estimates what we think it should estimate. I also show how to cluster standard errors in R. Here are some resources to which I refer in the video: I posted these scripts on my econometrics website. metrics.tonycookson.com. They are called clusterFunctions.R and exampleofclusteringinR.R, and they are available for download there. Mahmoud Arai's Clustering Functions. http://people.su.se/~ma/clustering.pdf Mitchell Peterson's Test Data (kept referring to him as Thompson in the video, sorry!). http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.htm And some background reading on this. Peterson 2009 can be found here in earlier WP version. http://ideas.repec.org/p/nbr/nberwo/11280.html Thompson 2011 can be found here. http://schwert.ssb.rochester.edu/f532/JFE11_ST.pdf To address the question about ML/REML. Here is a useful set of slides on how to specify the likelihood. http://www.stat.wisc.edu/~ane/st572/notes/lec21.pdf As I remember it, REML focuses on estimating the error components independently of the systematic part of the regression model. On an intuitive level, this amounts to building in a degrees of freedom correction. There is much more out there on a Google search of "REML vs. ML in random effects"
Views: 9279 intromediateecon
decision tree example(ID3)
 
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Download this sum PDF from link below http://britsol.blogspot.in/2017/10/decision-tree-algorithm-pdf.html?m=1 book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar
Views: 57501 fun 2 code
Fetching data from database using PHP - Full stack web development
 
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In this video we are using PHP to fetch data from our database we made earlier in previous videos. Please like share and subscribe
Views: 1405 Rameez Safdar
E05 - Data Preprocessing Topics - Machine learning course free ( Data Science Alive )
 
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*For the playlist , please click the below Link: https://www.youtube.com/watch?v=L7tEs7kraTQ&list=PL-1QQC56x1gGsEWQP1dZ4rOQvKyUhdttW #Data_science_alive #Machine_learning #No_1_Trending_video #Machine_learning_Python_R *Visit Our website : https://datasciencealive.wordpress.com/machine-learning/ *Please click the following link to download the dataset: https://datasciencealive.wordpress.com/data-set/ *In this session we will look into topics that will be covered on the data preprocessing techniques using pandas in python . In machine learning most of the time will be spend on data preprocessing , data mining and feature extraction . Hence please listen to this topic more carefully . *This is a Data science course . This is a full fledge course for free and we will cove all the main topics on the machine learning algorithm. This course is specifically designed to address all the queries from beginners to expert . Artificial intelligence ( AI ) is a bigger umbrella ,In that Machine learning ( ML ) and Deep Learning ( DL ) are part of Artificial Intelligence. *In this video we will have an overview on the topics that will be covered. On high level it will be *Data Preprocessing *Supervised Learning - Algorithm *Classification *Regression *Association *Unsupervised learning - Algorithm *Clustering *Dimensionality Reduction (PCA) *Semi -Supervised learning *Re- Enforcement learning *Best approach for Model selection *Intro to Deep Learning The above topics will be covered in-detail on the upcoming session which you can find it in the playlist . *For the playlist , please click the below Link: https://www.youtube.com/watch?v=L7tEs7kraTQ&list=PL-1QQC56x1gGsEWQP1dZ4rOQvKyUhdttW #Data_science_alive #Machine_learning #Machine_learning_Python_R #No_1_Trending_video
Views: 86 Data Science Alive
How to Create a File Association in Microsoft Excel : Tips for Microsoft Excel
 
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Subscribe Now: http://www.youtube.com/subscription_center?add_user=ehowtech Watch More: http://www.youtube.com/ehowtech Creating a file association in Microsoft Excel will have those types of files automatically open in the Excel program. Create a file association in Microsoft Excel with help from an experienced computer services technician in this free video clip. Expert: William Fisher Bio: William Fisher is an IT professional serving in such roles as IT Instructor, IT Trainer, Project Manager, Network Engineer, Technology Consultant and Computer Services Technician. Filmmaker: Jeff Goodey Series Description: Microsoft Excel is still one of the most powerful spreadsheet creation tools on the planet, even years after its original release. Get tips for Microsoft Excel with help from an experienced computer services technician in this free video series.
Views: 1702 eHowTech
WEKA - Birliktelik Analizi
 
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Weka, Weka Analiz, WEKA - Birliktelik Analizi TAGS veri madenciliği yöntemleri, veri madenciliği nedir, microsoft excell, makro nedir, makrolarla excell dersleri, mining data, mining, datamining, what is data, data mining pdf, data mining techniques, data analysis, mineria, mineria de datos, data warehouse, data warehousing, database, data mining algorithm, data mining ppt, database mining, data mining software, big data, clustering, data mining tools, google data mining, classification, big data mining, smite, datamining smite, smite data mining, coursera, smite reddit, smite patch notes, smite wiki,0 gw2 data mining, big data analytics, bigdata, big data mining, big data, slideshare, kaggle, data scientist, hadoop,0 data mining meaning, jurnal data mining, python data mining, data mining adalah, nptel, python, data mining pdf TAGS https://www.kodkolik.net/ Weka, makine öğrenimi amacıyla Waikato Üniversitesinde geliştirilmiş ve "Waikato Environment for Knowledge Analysis" kelimelerinin baş harflerinden oluşmuş yazılımın ismidir. Günümüzde yaygın kullanımı olan çoğu makine öğrenimi algoritmalarını ve metotlarını içermektedir. Java dilinde geliştirilmiş olması ve kütüphanelerinin .jar dosyaları halinde geliyor olması sayesinde, Java dilinde yazılan projelere kolayce entegre edilebilmesi kullanımını daha da yaygınlaştırmıştır Yazılım, GNU Genel Kamu Lisansı ile dağıtılmaktadır. Weka, tamamen modüler bir tasarıma sahip olup, içerdiği özelliklerle veri kümeleri üzerinde görselleştirme, veri analizi, iş zekası uygulamaları, veri madenciliği gibi işlemler yapabilmektedir. Weka yazılımı, kendisine özgü olarak bir .arff uzantısı desteği ile gelmektedir. Ancak Weka yazılımının içerisinde CSV dosyalarını da ARFF formatına çevirmeye yarayan araçlar mevcuttur. Temel olarak aşağıdaki 3 Veri Madenciliği işlemi Weka ile yapılabilir: Sınıflandırma (Classification) Bölütleme (Clustering) İlişkilendirme (Association) Ayrıca yukarıdaki işlemlere ilave olarak, veri kümeleri üzerinde ön ve son işlemler yapılabilir Veri Ön işleme (Data Pre-Processing) Görselleme (Visualization) Son olarak Weka Kütüphanesi'nde veri kümelerini içeren dosyalar üzerinde çalışan çok sayıda hazır fonksiyon bulunmaktadır. Machine Learning Group at the University of Waikato Project Software Book Publications People Related Weka 3: Data Mining Software in Java Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this. Weka is open source software issued under the GNU General Public License. We have put together several free online courses that teach machine learning and data mining using Weka. Check out the website for the courses for details on when and how to enrol. The videos for the courses are available on Youtube. Yes, it is possible to apply Weka to big data!
Data Entry & Internet Research
 
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* Data Scraping || Data Extraction || Data Crawling || Data Mining from any source of Website. * Data Scraping & Extraction - Copy Paste data from any Website * Data Mining & Crawling from Yellowpages, Yelp, Yell, Whitepages, etc.. * PDF to MS-Excel / MS-Word data Conversion. * Online Research || Market Research || Specific Niche Research || Product Research. * Researching Contact Details of specific industry / persons / professional for particular Titles.
Views: 130 Numb3r TekSolution