Incremental Rough Possibilistic K-Modes

In this paper, we propose a novel version of the k-modes method dealing with the incremental clustering under uncertain framework. The proposal is called the incremental rough possibilistic k-modes (I-RPKM). First, possibility theory is used to handle uncertain values of attributes in databases and, to compute the membership values of objects to resulting clusters. After that, rough set theory is applied to detect boundary regions. After getting the final partition, the I-RPKM adapts the incremental clustering strategy to take into account new information and update the cluster number without re-clustering objects. I-RPKM is shown to perform better than other certain and uncertain approaches.