Data Mining: Practical Machine Learning Techniques (2nd Edition)

Author: (New Zealand) Witten (Witten I.H.)
Publisher:
Publish Date: 2005-09-01
Features: "This book presents this new discipline to readers in a very easy-to-understand way: it is both a textbook for training the next generation of practitioners and researchers, and also highly enlightening for professional readers like me who need continuous learning. Witten and Frank are passionate about simple and smooth solutions, always remembering to build all concepts on concrete examples. They encourage readers to first consider simple techniques, and if these are not sufficient to solve the problem, then move on to more advanced and mature techniques. If you want to analyze and understand data, this book and the related Weka toolkit will be very useful." - Excerpted from the preface by Jim Gray, a Turing Award winner from Microsoft Research. This book has undergone significant changes from the first edition in 1999. Although the core concepts remain the same, the book has been updated to reflect the changes over the past five years, and the references have almost doubled. Important parts of the new edition include: 30 new chapters on techniques; an enhanced Weka machine learning workbench with an interactive interface; complete information on neural networks, a new section on Bayesian networks, and so on. This book provides a complete foundation for machine learning concepts, as well as suggestions for applying relevant tools and techniques in practical work. In this book, you will find: ● Core algorithms of successful data mining techniques, including proven real-world techniques and cutting-edge methods. ● Methods for transforming input or output to improve performance. ● Downloadable Weka software, a collection of machine learning algorithms for data mining tasks, including tools for data preprocessing, classification, regression, clustering, association rules, and visualization on a new interactive interface.