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 here
