Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications (Studies in Fuzziness and Soft Computing)

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 diferent fields of sciences are considering the tool of data clustering to be useful. A major reason is that clustering algorithms and software are flexible in the sense that different mathematical frameworks are employed in the algorithms and a user can select a suitable method according to his application. Moreover clustering algorithms have different out puts ranging from the old dendrograms of agglome- rative clustering to more recent self-organizing maps. Thus, a researcher or user can choose an appropriate output suited to his purpose, which is another flexibility 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 reason why we concentrate on fuzzy c-means is that most methodology and application studies in fuzzy clustering use fuzzy c-means,and fuzzy c-means should be considered to be a major technique of clustering in general,r egard less whe ther one is interested in fuzzy methods or not. Moreover recent advances in clustering techniques are rapid and we require a 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.