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1. Books

1.1. Applied Data Mining - Statistical Methods for Business and Industry

1.1.1. by Paolo Giudici

1.2. Data Mining Concepts and Techniques

1.2.1. by Jiawei Han and Micheline Kamber

1.3. Data Mining - Practical Machine Learning Tools and Techniques Third Edition

1.3.1. by Ian H. Witten, Eibe Frank, and Mark A. Hall

1.4. Data Mining Techniques - For Marketing, Sales, and Customer Relationship Management, 2nd Edition

1.4.1. by Michael J.A. Berry and Gordon S. Linoff

1.5. Discovering Knowledge in Data - An Introduction to Data Mining

1.5.1. by Daniel T. Larose

1.6. Handbook of Statistical Analysis and Data Mining Applications

1.6.1. by Robert Nisbet, John Elder, and Gary Miner

1.7. Intelligent Data Analysis - An Introduction

1.7.1. by Daniel T. Larose

1.8. Introduction to Data Mining

1.8.1. by Kumar, Steinbach and Tan

1.9. Making Sense of Data II - A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications

1.9.1. by Glenn J. Myatt and Wayne P. Johnson

1.10. Mining the Web: Transforming Customer Data into Customer Value

1.10.1. by Gordon S. Linoff and Michael J.A. Berry

2. Free Ebooks

2.1. A Programmer's Guide to Data Mining

2.1.1. Download link

2.2. An Introduction to Genetic Algorithms

2.2.1. Download link

2.3. Data Mining for the Masses

2.3.1. Download link

2.4. CRISP-DM 1.0

2.4.1. Download link

2.5. Data Jujitsu: The Art of Turning Data into a Product

2.5.1. Download link

2.6. Deep Learning

2.6.1. Download link

2.7. Deep Learning Methods and Applications

2.7.1. Download link

2.8. Deep Learning Tutorial, Release 1.0

2.8.1. Download link

2.9. Introduction to Information Retrieval

2.9.1. Download link

2.10. KB - Neural Data Mining with Python Source

2.10.1. Download link

2.11. Machine Learning Cheatsheet

2.11.1. Download link

2.12. Neural Networks and Deep Learning

2.12.1. Download link

2.13. Prolog for Programmers

2.13.1. Download link

2.14. Social Media Mining: An Introduction

2.14.1. Download link

2.15. Social Media Mining, An introduction

2.15.1. Download link

2.16. The Elements of Statistical Learning - Data Mining, Inference, and Prediction

2.16.1. Download link

2.17. Top 10 Algorithms in Data Mining

2.17.1. Download link

3. Open Source Publications Sites

3.1. InTech

3.1.1. Link


4.1. R

4.1.1. Website link

4.1.2. Tutorial Sites R Tutorial R-tutorials R tutorial to statistical models Beginner's guide to R: Introduction Resources to help you learn and use R Online R resources for Beginners Getting Started with the R Data Analysis Package Free R tutorials Quick-R Getting started with R

4.1.3. Tutorial EBooks & Pdf An Introduction to R R for Beginners University of North Texas R - Statistical and Graphical Software Notes University of California, Davis R for Programmers Florida State University R programming basics University of California, Santa Barbara R: A self-learn tutorial University of Notre Dame Introduction to R University of Auckland R Programming Basic Concepts Duke University Brief R Tutorial University of Warwick Programming Exercises for R University of Washington R Fundamentals and Programming Techniques University of California, Berkeley An Introduction to R

4.1.4. Tutorial Videos Stanford University Introduction to R Programming Part 1 introduction to R programming part 2 University of Colorado Denver R Basics Importing Data Basic analysis Plotting Installing Packages Texas A&M University R videos An Introduction to R A Brief Tutorial for R

4.1.5. Tutorial ppt

4.2. Python

4.2.1. Website link

4.3. Anaconda Python

4.3.1. Website link

4.4. Weka

4.4.1. Website link

4.5. RapidMiner

4.5.1. Website link

4.6. KNIME

4.6.1. Website link

4.7. Orange Canvas

4.7.1. Website link