Pattern Recognition Algorithms for Data Mining

Sanker K. Pal, Pabitra Mitra

244 pages, parution le 21/06/2004

Ajouter à une liste

Indisponible

Résumé

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.

Introduces pattern recognition (PR) concepts and tasks including scalability to large data sets and knowledge discovery in databases (KDD)
Describes methodologies for multiscale data condensation and unsupervised dimentionality reduction for large data
Presents active learning strategies for handling a large quadratic problem in an SVM framework
Deploys a rough-fuzzy framework for case generation and for clustering large data sets
Describes design procedure of a rough self-organizing map (RSOM)
Applies fuzzy sets, rough sets, neural nets, and genetic algorithms for problems of classification, rule generation, and evaluation in a supervised mode
Provides experimental results on real life data along with their sources

L'auteur - Sanker K. Pal

Sankar K Pal : Indian Statistical Institute, Calcutta, India

L'auteur - Pabitra Mitra

Pabitra Mitra : Indian Institute of Technology, Kanpur, India

Sommaire

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

Appendix A: Role Of Soft-Computing Tools In Kdd
Appendix B Data Sets Used In Experiments

Voir tout

Replier

Caractéristiques techniques

	PAPIER
Éditeur(s)	Chapman and Hall / CRC
Auteur(s)	Sanker K. Pal, Pabitra Mitra
Parution	21/06/2004
Nb. de pages	244
Format	16 x 24
Couverture	Relié
Poids	525g
Intérieur	Noir et Blanc
EAN13	9781584884576
ISBN13	978-1-58488-457-6