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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
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
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