Introduction. Multiscale data condensation. Unsupervised feature selection. Active learning using support vector machine. Rough-fuzzy case generation. Rough-fuzzy clustering. Rough self-organizing map. Classification, rule generation and evaluation using modular rough-fuzzy MLP. Appendices.

"Pattern Recognition Algorithms in Data Mining is a book that commands admiration. Its authors, Professors S.K. Pal and P. Mitra are foremost authorities in pattern recognition, data mining, and related fields. Within its covers, the reader finds an exceptionally well-organized exposition of every concept and every method that is of relevance to the theme of the book. There is much that is original and much that cannot be found in the literature. The authors and the publisher deserve our thanks and congratulations for producing a definitive work that contributes so much and in so many important ways to the advancement of both the theory and practice of recognition technology, data mining, and related fields. The magnum opus of Professors Pal and Mitra is must-reading for anyone who is interested in the conception, design, and utilization of intelligent systems." - from the Foreword by Lotfi A. Zadeh, University of California, Berkeley, USA "The book presents an unbeatable combination of theory and practice and provides a comprehensive view of methods and tools in modern KDD. The authors deserve the highest appreciation for this excellent monograph." - from the Foreword by Zdzislaw Pawlak, Polish Academy of Sciences, Warsaw " This volume provides a very useful, thorough exposition of the many facets of this application from several perspectives. … I congratulate the authors of this volume and I am pleased to recommend it as a valuable addition to the books in this field." - from the Forword by Laveen N. Kanal, University of Maryland, College Park, USA.