Hardback : £87.23
Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks.
Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.
Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks.
Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.
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.
Pal, Sankar K.; Mitra, Pabitra
"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.
|
Ask a Question About this Product More... |
|
