
Algorithms for Fuzzy Clustering
Methods in c-Means Clustering with Applications
Springer (Publisher)
Published on 15. April 2008
Book
Hardback
XI, 247 pages
978-3-540-78736-5 (ISBN)
Description
Recently many researchers are working on cluster analysis as a main tool for exploratory data analysis and data mining. A notable feature is that specialists in di?erent ?elds of sciences are considering the tool of data clustering to be useful. A major reason is that clustering algorithms and software are ?exible in thesensethatdi?erentmathematicalframeworksareemployedinthealgorithms and a user can select a suitable method according to his application. Moreover clusteringalgorithmshavedi?erentoutputsrangingfromtheolddendrogramsof agglomerativeclustering to more recent self-organizingmaps. Thus, a researcher or user can choose an appropriate output suited to his purpose,which is another ?exibility of the methods of clustering. An old and still most popular method is the K-means which use K cluster centers. A group of data is gathered around a cluster center and thus forms a cluster. The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reasonwhy we concentrate on fuzzy c-means is that most methodology and application studies infuzzy clusteringusefuzzy c-means,andfuzzy c-meansshouldbe consideredto beamajortechniqueofclusteringingeneral,regardlesswhetheroneisinterested in fuzzy methods or not. Moreover recent advances in clustering techniques are rapid and we requirea new textbook that includes recent algorithms.We should also note that several books have recently been published but the contents do not include some methods studied herein.
More details
Series
Edition
2008 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
XI, 247 p.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 19 mm
Weight
559 gr
ISBN-13
978-3-540-78736-5 (9783540787365)
DOI
10.1007/978-3-540-78737-2
Schweitzer Classification
Other editions
Additional editions

Sadaaki Miyamoto | Hidetomo Ichihashi | Katsuhiro Honda
Algorithms for Fuzzy Clustering
Methods in c-Means Clustering with Applications
Book
11/2010
Springer
€160.49
Shipment within 7-9 days
Persons
Dr. Miyamoto was born in Osaka, Japan, in 1950. He received the B.S., M.S., and the Dr. Eng. degrees in Applied Mathematics and Physics Engineering from Kyoto University, Japan, in 1973, 1975, and 1978, respectively. He was Assistant Professor from 1980 to 1987 and Associate Professor from 1987 to 1990 in the University of Tsukuba. He was Professor with the Faculty of Engineering, the University of Tokushima, where he was working from 1990 to 1994. After working as Professor at the University of Tsukuba from 1994, he retired on March 31, 2016, and became Professor Emeritus from April 1, 2016.
His research interests include methodology for fuzzy systems and uncertainty modeling. In particular, he has been working on data clustering algorithms and related classification methods, multisets, rough sets, and algorithms for data mining. He is Member of the Japan Society of Fuzzy Theory and Systems, and Japanese Classification Society. He has served a number of internationalconferences as Chair, Co-chair, and Committee Member. He received excellent paper awards from the Japan Society of Fuzzy Theory and Systems in 1994 and 1999. He has published three books of which two are in English and the other in Japanese. He also has published one edited book and over 300 research papers. His papers/books have been cited more than 2,000 times. He became a fellow of International Fuzzy Systems Association in 2007. He was also elected to be a fellow of Japanese Classification Society in 2017.
Content
BasicMethods 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.