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What is Data Mining?

NJIT School of Management professor Stephan P Kudyba describes what data mining is and how it is being used in the business world.
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Rescooped by Sebastien Louchart from BIG data, Data Mining, Predictive Modeling, Visualization
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Big Data Analytics: Descriptive Vs. Predictive Vs. Prescriptive - InformationWeek

Big Data Analytics: Descriptive Vs. Predictive Vs. Prescriptive - InformationWeek | Machine learning | Scoop.it
Big Data Analytics: Descriptive Vs. Predictive Vs.

Via AnalyticsInnovations
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Rescooped by Sebastien Louchart from IT Books Free Share
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Pentaho for Big Data Analytics - PDF Free Download - Fox eBook

Pentaho for Big Data Analytics - PDF Free Download - Fox eBook | Machine learning | Scoop.it
Pentaho for Big Data Analytics PDF Free Download, Reviews, Read Online, ISBN: 1783282150, By Feris Thia, Manoj R Patil

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Fox eBook's curator insight, May 12, 2014 10:31 PM

Chapter 1: The Rise of Pentaho Analytics along with Big Data
Chapter 2: Setting Up the Ground
Chapter 3: Churning Big Data with Pentaho
Chapter 4: Pentaho Business Analytics Tools
Chapter 5: Visualization of Big Data
Appendix A: Big Data Set
Appendix B: Hadoop Setup

Rescooped by Sebastien Louchart from Digital Humanities and Linked Data
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A Programmer's Guide to #DataMining I #OpenBook #DataScience

A Programmer's Guide to #DataMining I #OpenBook #DataScience | Machine learning | Scoop.it

Via Joaquín Herrero Pintado, Toni Sánchez, luiy, Intriguing Networks
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luiy's curator insight, December 8, 2013 2:51 PM

Table of Contents

 

This book’s contents are freely available as PDF files. When you click on a chapter title below, you will be taken to a webpage for that chapter. The page contains links for a PDF of that chapter and for any sample Python code and data that chapter requires. Please let me know if you see an error in the book, if some part of the book is confusing, or if you have some other comment. I will use these to revise the chapters.

 

Chapter 1: Introduction

 

Finding out what data mining is and what problems it solves. What will you be able to do when you finish this book.

 

Chapter 2: Get Started with Recommendation Systems

 

Introduction to social filtering. Basic distance measures including Manhattan distance, Euclidean distance, and Minkowski distance. Pearson Correlation Coefficient. Implementing a basic algorithm in Python.

 

Chapter 3: Implicit ratings and item-based filtering

 

A discussion of the types of user ratings we can use. Users can explicitly give ratings (thumbs up, thumbs down, 5 stars, or whatever) or they can rate products implicitly–if they buy an mp3 from Amazon, we can view that purchase as a ‘like’ rating.

Chapter 4: Classification

 

In  previous chapters we used  people’s ratings of products to make recommendations. Now we turn to using attributes of the products themselves to make recommendations. This approach is used by Pandora among others.

 

Chapter 5: Further Explorations in Classification

 

A discussion on how to evaluate classifiers including 10-fold cross-validation, leave-one-out, and the Kappa statistic. The k Nearest Neighbor algorithm is also introduced.

 

Chapter 6: Naïve Bayes

 

An exploration of Naïve Bayes classification methods. Dealing with numerical data using probability density functions.

 

Chapter 7: Naïve Bayes and unstructured text

 

This chapter explores how we can use Naïve Bayes to classify unstructured text. Can we classify twitter posts about a movie as to whether the post was a positive review or a negative one? (new version coming November 2013)

Intriguing Networks's curator insight, December 8, 2013 5:48 PM

Cheers thanks for this handy for all budding DH students

Rescooped by Sebastien Louchart from PDG-Technologies
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Making Big Data Analytics Interactive and Real-­Time

Big  Data  Analytics  Interactive  and  Real-­‐Time


Via SevenNguyen
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