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Introduction to Data Mining: Basic Data Types
 
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Continuing our series on Data Mining Fundamentals, we introduce you to the three data set types, Record, Ordered, and Graph and give you examples of when you would want to use each data set. -- 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/H0f8Lkc0 See what our past attendees are saying here: https://hubs.ly/H0f8Lkk0 -- 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_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 11351 Data Science Dojo
Data Mining & Business Intelligence | Tutorial #4 | Forms Of Data Preprocessing
 
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Order my books at 👉 http://www.tek97.com/ #RanjiRaj #DataMining #DataPreprocessing Its important to preprocess the data before processing. Have a look at different forms of data preprocessing in data mining. Watch now ! Il est important de prétraiter les données avant le traitement. Jetez un oeil à différentes formes de prétraitement des données dans l'exploration de données. Regarde maintenant ! Es ist wichtig, die Daten vor der Verarbeitung vorzuverarbeiten. Sehen Sie sich verschiedene Formen der Datenvorverarbeitung im Data Mining an. Schau jetzt ! Es importante preprocesar los datos antes del procesamiento. Eche un vistazo a las diferentes formas de preprocesamiento de datos en la minería de datos. Ver ahora ! من المهم أن preprocess البيانات قبل المعالجة. إلقاء نظرة على أشكال مختلفة من معالجة البيانات في تعدين البيانات. شاهد الآن ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ 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: 4455 Ranji Raj
data mining techniques
 
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This video describes data mining tasks or techniques in brief. Each technique requires a separate explanation as well. #datamining #techniques #weka Data mining tutorial in hindi Weka tutorial in hindi Data mining tutorial
Views: 5033 yaachana bhawsar
Data Mining, Classification, Clustering, Association Rules, Regression, Deviation
 
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Complete set of Video Lessons and Notes available only at http://www.studyyaar.com/index.php/module/20-data-warehousing-and-mining Data Mining, Classification, Clustering, Association Rules, Sequential Pattern Discovery, Regression, Deviation http://www.studyyaar.com/index.php/module-video/watch/53-data-mining
Views: 86531 StudyYaar.com
Introduction to data mining and architecture  in hindi
 
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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: 198998 Last moment tuitions
Data Analytics - Descriptive , Predictive and Prescriptive Analytics
 
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@ Members ~ This video would let you know about rising importance of Analytics where by we are covering all 4 Branches of Analytics like Financial Analytics , Risk Based Analytics , Cash Flow Analytics and Data Analytics. Video would also let you know about 3 types of Analytics covering Descriptive Analytics , Predictive Analytics and Prescriptive Analytics. You are most welcome to connect with us at 91-9899242978 (Handheld) , Skype ~ Rahul5327 , Twitter @ Rahulmagan8 , [email protected] , [email protected] or visit our website - www.treasuryconsulting.in
Data Mining - Clustering
 
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What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
Types of Data: Nominal, Ordinal, Interval/Ratio - Statistics Help
 
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The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples. Subtitles in English and Spanish.
Views: 848576 Dr Nic's Maths and Stats
How data mining works
 
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In this video we describe data mining, in the context of knowledge discovery in databases. More videos on classification algorithms can be found at https://www.youtube.com/playlist?list=PLXMKI02h3_qjYoX-f8uKrcGqYmaqdAtq5 Please subscribe to my channel, and share this video with your peers!
Views: 223379 Thales Sehn Körting
Introduction to Data Mining: Types of Sampling
 
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In this Data Mining Fundamentals tutorial, we discuss the different types of sampling for data preprocessing, such as random sampling, stratified sampling, sampling without and with replacement. We will also dive into the issues of sample size, and how that can effect your sampling. -- 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/H0f8LpT0 See what our past attendees are saying here: https://hubs.ly/H0f8Lqf0 -- 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: 5963 Data Science Dojo
Last Minute Tutorials | Data mining | Introduction | Examples
 
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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: 41393 Last Minute Tutorials
Introduction to Data Mining: Similarity & Dissimilarity
 
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In this Data Mining Fundamentals tutorial, we introduce you to similarity and dissimilarity. Similarity is a numerical measure of how alike two data objects are, and dissimilarity is a numerical measure of how different two data objects are. We also discuss similarity and dissimilarity for single attributes. -- 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/H0f8Lsn0 See what our past attendees are saying here: https://hubs.ly/H0f8Lsp0 -- 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: 15929 Data Science Dojo
Data Mining Lecture -- Bayesian Classification | Naive Bayes Classifier | Solved Example (Eng-Hindi)
 
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In the bayesian classification The final ans doesn't matter in the calculation Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result. -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 157459 Well Academy
Machine Learning - Supervised VS Unsupervised Learning
 
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Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/ Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning. This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed! Explore many algorithms and models: Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction. Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine! Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE •This course is free. •It is self-paced. •It can be taken at any time. •It can be audited as many times as you wish. https://bigdatauniversity.com/courses/machine-learning-with-python/
Views: 78506 Cognitive Class
Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 29510 Red Apple Tutorials
Kinds of data
 
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Kinds of data: Categorical (nominal & ordinal) and numerical (discrete & continuous)
Views: 66990 Ian Bailey-Mortimer
6 Types of Classification Algorithms
 
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Here are some of the most commonly used classification algorithms -- Logistic Regression, Naïve Bayes, Stochastic Gradient Descent, K-Nearest Neighbours, Decision Tree, Random Forest and Support Vector Machine. https://analyticsindiamag.com/7-types-classification-algorithms/ -------------------------------------------------- Get in touch with us: Website: www.analyticsindiamag.com Contact: [email protected] Facebook: https://www.facebook.com/AnalyticsIndiaMagazine/ Twitter: http://www.twitter.com/analyticsindiam Linkedin: https://www.linkedin.com/company-beta/10283931/ Instagram: https://www.instagram.com/analyticsindiamagazine/
Data Mining Functionalities
 
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data warehousing and data mining || data mining funtionalities
Views: 4614 naga mounika Reddy
KDD ( knowledge data discovery )  in data mining in hindi
 
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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: 67684 Last moment tuitions
Introduction to Data Mining: Data Attributes (Part 1)
 
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In this video tutorial on Data Mining Fundamentals, we dive deeper into the vocabulary used in data mining, focusing on attributes. By the end of this tutorial, you will understand the different kinds of attribute classification, and when you should use each. -- 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/H0f8Lh-0 See what our past attendees are saying here: https://hubs.ly/H0f8LXl0 -- 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: 14377 Data Science Dojo
Four Types Of Cross Validation| K-Fold | Leave One Out |Bootstrap | Hold Out
 
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In this video you will learn about the different types of cross validation you can use to validate you statistical model. Cross validation is an important step in model building which ensures you have a model that will perform well in the new data , which also overcomes the possibility model being over fit. There are four types of cross validation you will learn 1- Hold out Method 2- K-Fold CV 3- Leave one out CV 4-Bootstrap Methods for more learn here : https://www.cs.cmu.edu/~schneide/tut5/node42.html Cross validation is also important step in machine learning model building and data science model building. Study Packs : https://analyticuniversity.com Facebook : https://www.facebook.com/AnalyticsUniversity Twitter : https://twitter.com/AnalyticsUniver
Views: 37204 Analytics University
An Overview of Association Rules
 
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Introduction to Association Rules My web page: www.imperial.ac.uk/people/n.sadawi
Views: 52776 Noureddin Sadawi
Data Mining Techniques
 
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Decision Trees, Naive Bayes, and Neural Networks
Views: 22412 nathan baughman
Difference between Classification and Regression - Georgia Tech - Machine Learning
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-313488098/m-674518790 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 73891 Udacity
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.
Classification of Data Mining Problems v1
 
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I will explain 9 common data mining problem types. The information in this presentation is mostly based on the great book called "Data Science for Business" written by Provost and Fawcett. http://datascience.mertnuhoglu.com Please give positive or negative feedback on the presentation. Does it help? What do you suggest to make it better?
Views: 9022 Mert Nuhoglu
Data Mining Classification - Basic Concepts
 
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Classification in Data Mining with classification algorithms. Explanation on classification algorithm the decision tree technique with Example.
Data Mining   KDD Process
 
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KDD - knowledge discovery in Database. short introduction on Data cleaning,Data integration, Data selection,Data mining,pattern evaluation and knowledge representation.
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: 207997 Last moment tuitions
Types of Sampling Methods (4.1)
 
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Get access to practice questions, written summaries, and homework help on our website! http://wwww.simplelearningpro.com Follow us on Instagram http://www.instagram.com/simplelearningpro Like us on Facebook http://www.facebook.com/simplelearningpro Follow us on Twitter http://www.twitter.com/simplelearningp If you found this video helpful, please subscribe, share it with your friends and give this video a thumbs up!
Views: 283918 Simple Learning Pro
Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
 
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Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 51733 Well Academy
Top 5 Algorithms used in Data Science | Data Science Tutorial | Data Mining Tutorial | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This tutorial will give you an overview of the most common algorithms that are used in Data Science. Here, you will learn what activities Data Scientists do and you will learn how they use algorithms like Decision Tree, Random Forest, Association Rule Mining, Linear Regression and K-Means Clustering. To learn more about Data Science click here: http://goo.gl/9HsPlv The topics related to 'R', Machine learning and Hadoop and various other algorithms have been extensively covered in our course “Data Science”. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 103071 edureka!
Data Mining Lecture -- Decision Tree | Solved Example (Eng-Hindi)
 
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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 174179 Well Academy
Data Preprocessing
 
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Project Name: Learning by Doing (LBD) based course content development Project Investigator: Prof Sandhya Kode
Views: 35157 Vidya-mitra
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: 441 Technology mart
Mining Multilevel Association Rules ll DMW ll Concept Hierarchy ll Explained with Examples in Hindi
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 10476 5 Minutes Engineering
K mean clustering algorithm with solve example
 
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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: 332889 Last moment tuitions
Introduction to Data Mining: Data Cleaning
 
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In this Data Mining Fundamentals tutorial, we introduce Data Preprocessing, known as data cleaning, and the different strategies used to tackle it. There are many strategies for data preprocessing, and because data science is such a heterogeneous field, none of these strategies are strictly independent. -- 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/H0f8M6d0 See what our past attendees are saying here: https://hubs.ly/H0f8Ln10 -- 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: 8387 Data Science Dojo
Data Mining Lecture -- Rule - Based Classification (Eng-Hindi)
 
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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 35745 Well Academy
Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
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In this video Apriori 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 in hindi, Finding frequent item sets, data mining, data mining algorithms in hindi, data mining lecture, data mining tools, data mining tutorial,
Views: 198409 Well Academy
Binning | Binning Method | Binning Algorithm | Binning In Data Mining
 
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Binning |Binning Method | Binning Algorithm | Binning In Data Mining ************************************************ the binding of isaac, binning , binnington, equal width binning, binning method, binning algorithm, bin data in r, bin data in excel, binning in excel, binning in data mining, data mining, data mining techniques, data mining tutorial, data mining algorithms, data mining course, data mining excel, r data minin, python data mining, Please Subscribe My Channel
Views: 13287 Learning With Mahamud
Meta data  in 5 mins hindi
 
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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: 104893 Last moment tuitions
Student Performance Measure By Using Different Classification Methods Of Data Mining
 
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This video explains about the various classification methods of data mining to measure the performance of the students using the grades obtained in four semesters.
Views: 2765 Neha Choudhary
Sampling: Simple Random, Convenience, systematic, cluster, stratified - Statistics Help
 
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This video describes five common methods of sampling in data collection. Each has a helpful diagrammatic representation. You might like to read my blog: https://creativemaths.net/blog/
Views: 733454 Dr Nic's Maths and Stats
Supervised & Unsupervised Learning
 
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In this video you will learn what are the differences between Supervised Learning & Unsupervised learning in the context of Machine Learning. Linear regression, Logistic regression, SVM, random forest are the supervised learning algorithms. For all videos and Study packs visit : http://analyticuniversity.com/ Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx
Views: 55398 Analytics University
Scales of Measurement - Nominal, Ordinal, Interval, Ratio (Part 1) - Introductory Statistics
 
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This video reviews the scales of measurement covered in introductory statistics: nominal, ordinal, interval, and ratio (Part 1 of 2). Scales of Measurement Nominal, Ordinal, Interval, Ratio YouTube Channel: https://www.youtube.com/user/statisticsinstructor Subscribe today! Lifetime access to SPSS videos: http://tinyurl.com/m2532td Video Transcript: In this video we'll take a look at what are known as the scales of measurement. OK first of all measurement can be defined as the process of applying numbers to objects according to a set of rules. So when we measure something we apply numbers or we give numbers to something and this something is just generically an object or objects so we're assigning numbers to some thing or things and when we do that we follow some sort of rules. Now in terms of introductory statistics textbooks there are four scales of measurement nominal, ordinal, interval, and ratio. We'll take a look at each of these in turn and take a look at some examples as well, as the examples really help to differentiate between these four scales. First we'll take a look at nominal. Now in a nominal scale of measurement we assign numbers to objects where the different numbers indicate different objects. The numbers have no real meaning other than differentiating between objects. So as an example a very common variable in statistical analyses is gender where in this example all males get a 1 and all females get a 2. Now the reason why this is nominal is because we could have just as easily assigned females a 1 and males a 2 or we could have assigned females 500 and males 650. It doesn't matter what number we come up with as long as all males get the same number, 1 in this example, and all females get the same number, 2. It doesn't mean that because females have a higher number that they're better than males or males are worse than females or vice versa or anything like that. All it does is it differentiates between our two groups. And that's a classic nominal example. Another one is baseball uniform numbers. Now the number that a player has on their uniform in baseball it provides no insight into the player's position or anything like that it just simply differentiates between players. So if someone has the number 23 on their back and someone has the number 25 it doesn't mean that the person who has 25 is better, has a higher average, hits more home runs, or anything like that it just means they're not the same playeras number 23. So in this example its nominal once again because the number just simply differentiates between objects. Now just as a side note in all sports it's not the same like in football for example different sequences of numbers typically go towards different positions. Like linebackers will have numbers that are different than quarterbacks and so forth but that's not the case in baseball. So in baseball whatever the number is it provides typically no insight into what position he plays. OK next we have ordinal and for ordinal we assign numbers to objects just like nominal but here the numbers also have meaningful order. So for example the place someone finishes in a race first, second, third, and so on. If we know the place that they finished we know how they did relative to others. So for example the first place person did better than second, second did better than third, and so on of course right that's obvious but that number that they're assigned one, two, or three indicates how they finished in a race so it indicates order and same thing with the place finished in an election first, second, third, fourth we know exactly how they did in relation to the others the person who finished in third place did better than someone who finished in fifth let's say if there are that many people, first did better than third and so on. So the number for ordinal once again indicates placement or order so we can rank people with ordinal data. OK next we have interval. In interval numbers have order just like ordinal so you can see here how these scales of measurement build on one another but in addition to ordinal, interval also has equal intervals between adjacent categories and I'll show you what I mean here with an example. So if we take temperature in degrees Fahrenheit the difference between 78 degrees and 79 degrees or that one degree difference is the same as the difference between 45 degrees and 46 degrees. One degree difference once again. So anywhere along that scale up and down the Fahrenheit scale that one degree difference means the same thing all up and down that scale. OK so if we take eight degrees versus nine degrees the difference there is one degree once again. That's a classic interval scale right there with those differences are meaningful and we'll contrast this with ordinal in just a few moments but finally before we do let's take a look at ratio.
Views: 337949 Quantitative Specialists
AN EFFICIENT PREDICTION OF CANCER USING DATA MINING TECHNIQUE
 
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Cancer is one of the major causes of death when compared to all other diseases. Cancer has become the most hazardous types of disease among the living creature in the world. Early detection of cancer is essential in reducing life losses. This work aims to establish an accurate classification model for Cancer prediction, in order to make full use of the invaluable information in clinical data. The dataset is divided into training set and test set. In this experiment, we compare six classification techniques in Weka software and comparison results show that Support Vector Machine (SVM) has higher prediction accuracy than those methods. Different methods for cancer detection are explored and their accuracies are compared. With these results, we infer that the SVM are more suitable in handling the classification problem of cancer prediction, and we recommend the use of these approaches in similar classification problems. This work presents a comparison among the different Data mining classifiers on the database of cancer, by using classification accuracy.
Views: 4525 David Clinton
INTRODUCTION TO DATA MINING IN HINDI
 
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Views: 108781 LearnEveryone