Fuzzy Sets and Their Application to Clustering and Training
Lakhmi C. Jain, Beatrice Lazzerini (Editor), D. DumitrescuBooks.org participates in affiliate programs including Bookshop.org and the Amazon Services LLC Associates Program. We may earn a commission from qualifying purchases made through links on this page, at no additional cost to you.
Synopsis
Fuzzy set theory - and its underlying fuzzy logic - represents one of the most significant scientific and cultural paradigms to emerge in the last half-century. Its theoretical and technological promise is vast, and we are only beginning to experience its potential. Clustering is the first and most basic application of fuzzy set theory, but forms the basis of many, more sophisticated, intelligent computational models, particularly in pattern recognition, data mining, adaptive and hierarchical clustering, and classifier design.
Fuzzy Sets and their Application to Clustering and Training offers a comprehensive introduction to fuzzy set theory, focusing on the concepts and results needed for training and clustering applications. It provides a unified mathematical framework for fuzzy classification and clustering, a methodology for developing training and classification methods, and a general method for obtaining a variety of fuzzy clustering algorithms.
The authors - top experts from around the world - combine their talents to lay a solid foundation for applications of this powerful tool, from the basic concepts and mathematics through the study of various algorithms, to validity functionals and hierarchical clustering. The result is Fuzzy Sets and their Application to Clustering and Training - an outstanding initiation into the world of fuzzy learning classifiers and fuzzy clustering.
Booknews
A textbook for a one-semester graduate course in pattern recognition and fuzzy technology, and a general introduction to the concepts and practices of ambiguity-tolerant reasoning for readers interested in intelligent computation models and particularly pattern recognition, data mining, clustering, and classifier design. Provides an adequate and unitary mathematical framework for fuzzy classification and clustering, general methodologies for developing fuzzy training and classification methods and obtaining a large variety of fuzzy clustering algorithms, and some basic algorithms. Annotation c. Book News, Inc., Portland, OR (booknews.com)