A Fuzzy Clustering Method Using the Relative Structure of the Belongingness of Objects to Clusters

With the advancement of information processing technology in recent years, larger and more complicated data has appeared. On the basis of this situation, a method to deal with this kind of data is required. Cluster analysis, or clustering will be one solution. There are two types of data in a clustering method. One is the data that consists of objects and attributes, the other is the data that consists of the similarity of each object. The latter, a data of similarity is treated in this study. The purpose of the clustering for similarity data is to obtain the clustering result based on the similarity scaling among objects. However, when the data is complex the given similarity data does not always have the structure of similarity scaling defined in the clustering method. Therefore, in this paper, a fuzzy clustering method that enables us to obtain a clear classification for the complex data is proposed, by introducing the similarity data to the obtained clustering result and considering the relative structure for all the clusters. By considering the relative structure of the belongingness to clusters, more specific information of objects can be given, and the belongingness would be improved