Algorithms for fuzzy clustering: methods in c-means clustering with applications

The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reason why we concentrate on fuzzy c-means is that most methodology and application studies in fuzzyclustering use fuzzy c-means, and hence fuzzy c-means should be considered tobe a major technique of clustering in general, regardless whether one is interested in fuzzy methods or not. Unlike most studies in fuzzy c-means, what we emphasize in this book is a family of algorithms using entropy or entropy-regularized methods which are less known, but we consider the entropy-based methodto be another useful method of fuzzy c-means. Throughout this book one of ourintentions is to uncover theoretical and methodological differences between the Dunn and Bezdek traditional method and the entropy-based method. Presents recent advances in algorithms for fuzzy clustering INDICE: Basic Methods for c-Means Clustering.- Variations and Generalizations – I.- Variations and Generalizations – II.- Miscellanea.- Application to Classifier Design.- Fuzzy Clustering and Probabilistic PCA Model.- Local Multivariate Analysis Based on Fuzzy Clustering.- Extended Algorithms for Local Multivariate Analysis.

• ISBN: 978-3-540-78736-5
• Editorial: Springer
• Encuadernacion: Cartoné
• Páginas: 255
• Fecha Publicación: 01/04/2008
• Nº Volúmenes: 1
• Idioma: Inglés