Establishing the method of rough set clustering

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Traditional rough set theory is concerned with supervised classification of decision tables, with the exception of a few proposals of unsupervised classification, in other words, clustering related to rough sets. However, there are many clustering techniques that had not been applied to rough sets before this study. We have studied theoretical background of clustering for rough sets, and developed a number of new algorithms including both hierarchical and non-hierarchical clustering. We moreover applied the developed methods to a number of data sets including those in medical information. We thus have shown a broad overview of clustering techniques of rough sets and related applications.