Data Mining is an area that I have worked on extensively, both as part of my PhD research and as part of teaching. This page includes useful resources on Data Mining and Machine Learning, as well as information about the book that I have co-authored on applying machine learning algorithms in R (available here).

Books

Introduction to Data Mining
by Pang-Ning Tan, Michael Steinbach, Vipin Kumar

Comprehensive book on Data Mining

Book link: http://www-users.cs.umn.edu/~kumar/dmbook/index.php

Data Mining: Practical Machine Learning Tools and Techniques
by Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal

Practical approach to Data Mining

Book link: http://www.cs.waikato.ac.nz/ml/weka/book.html

Machine Learning
by Tom Mitchell

Classical book on Machine Learning

Book link: http://www.cs.cmu.edu/~tom/mlbook.html

Pattern Recognition and Machine Learning
by Christopher M. Bishop

Solid math book for Pattern Recognition

Book link: https://www.microsoft.com/en-us/research/people/cmbishop/#prml-book

Own material

Practical Machine Learning in R
by Kyriakos Chatzidimitriou, Themistoklis Diamantopoulos, Michail Papamichail, and Andreas Symeonidis

Book on Machine Learning algorithms practically presented using R

Book link: https://leanpub.com/practical-machine-learning-r

Links