Decremental rough possibilistic K-Modes

Abstract

This paper proposes decremental rough possibilitic k-modes (D-RPKM) as a new clustering method for categorical databases. It distinguishes itself from the conventional clustering method in four aspects. First, it can deal with uncertain values of attributes by defining possibility degrees. Then, it handles uncertainty when an object belongs to several clusters using possibilistic membership degrees. It also determines boundary regions through the computing of the approximation sets based on the rough set theory. Finally, it accommodates gradual changes in datasets where there is a decrease in the cluster number. Such a dynamically changing dataset can be seen in numerous real-world situations such as changing behaviour of customers, or popularity of products or when there is, for example, an extinction of some species or diseases. For experiments, we use UCI machine learning repository datasets with different evaluation criteria. Results highlight the effectiveness of the proposed method compared to different versions of k-modes method. © 2014 Springer International Publishing Switzerland.