Data Mining

This comprehensive textbook on data mining details the unique steps of the knowledge discovery process - an industry standard that prescribes the sequence in which projects should be performed, from data understanding and preprocessing to deployment of the results.

If you torture the data long enough, Nature will confess, said 1991 Nobel-winning economist Ronald Coase. The statement is still true. However, achieving this lofty goal is not easy. First, long enough may, in practice, be too long in many applications and thus unacceptable. Second, to get confession from large data sets one needs to use state-of-the-art torturing tools. Third, Nature is very stubborn not yielding easily or unwilling to reveal its secrets at all. Fortunately, while being aware of the above facts, the reader (a data miner) will find several efficient data mining tools described in this excellent book. The book discusses various issues connecting the whole spectrum of approaches, methods, techniques and algorithms falling under the umbrella of data mining. It starts with data understanding and preprocessing, then goes through a set of methods for supervised and unsupervised learning, and concludes with model assessment, data security and privacy issues. It is this specific approach of using the knowledge discovery process that makes this book a rare one indeed, and thus an indispensable addition to many other books on data mining. To be more precise, this is a book on knowledge discovery from data. As for the data sets, the easy-to-make statement is that there is no part of modern human activity left untouched by both the need and the desire to collect data. The consequence of such a state of affairs is obvious.

Provides suite of exercises Includes links to instructional presentations Contains appendices of relevant mathematical material Includes supplementary material: sn.pub/extras

Inhalt
Data Mining and Knowledge Discovery Process.- The Knowledge Discovery Process.- Data Understanding.- Data.- Concepts of Learning, Classification, and Regression.- Knowledge Representation.- Data Preprocessing.- Databases, Data Warehouses, and OLAP.- Feature Extraction and Selection Methods.- Discretization Methods.- Data Mining: Methods for Constructing Data Models.- Unsupervised Learning: Clustering.- Unsupervised Learning: Association Rules.- Supervised Learning: Statistical Methods.- Supervised Learning: Decision Trees, Rule Algorithms, and Their Hybrids.- Supervised Learning: Neural Networks.- Text Mining.- Data Models Assessment.- Assessment of Data Models.- Data Security and Privacy Issues.- Data Security, Privacy and Data Mining.