This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.
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.
From the reviews: "This is a comprehensive book about knowledge discovery methods. ... the book is highly recommended to final year undergraduate students, postgraduate students and lecturers. ... it has a good balance of various topics making it a good reference book for practitioners, such as data modellers, insight analysts, fraud analysts, etc., as well as researchers. ... this book is very well organized and presented. ... I would certainly recommend it to those with intermediate or advanced understanding of data-mining topics." (Boran Gazi, The Computer Journal, Vol. 53 (4), 2010)
Krzysztof J. Cios, Witold Pedrycz
Springer-Verlag New York Inc.
A Knowledge Discovery Approach
Place of Publication
New York, NY
Country of Publication
DATA MINING 2007/E