On objective-based rough c-means clustering

Conventional clustering algorithms classify a set of objects into some clusters with clear boundaries, that is, one object must belong to one cluster. However, many objects belong to more than one cluster in real world, since the boundaries of clusters overlap with each other. Fuzzy set representation of clusters makes it possible for each object to belong to more than one cluster. On the other hand, the fuzzy degree sometimes may be too descriptive for interpreting clustering results. Rough set representation could deal with such cases. Clustering based on rough set could provide a solution that is less restrictive than conventional clustering and less descriptive than fuzzy clustering. This paper proposes a rough clustering algorithm which is based on optimization of an objective function and the calculation formula of cluster centers is the same as one by Lingras. Moreover, it shows effectiveness of our proposed clustering algorithm in comparison with other algorithms.