Sequential Possibilistic One-Means Clustering with Dynamic Eta

The Possibilistic C-Means (PCM) was developed as an extension of the Fuzzy C-Means (FCM) by abandoning the membership sum-to-one constraint. In PCM, each cluster is independent of the other clusters, and can be processed separately. Thus, the Sequential Possibilistic One-Means (SP1M) was proposed to find clusters sequentially by running P1M C times. One critical problem in both PCM and SP1M is how to determine the parameter η. The Sequential Possibilistic One Means with Adaptive Eta (SP1M-AE) was developed to allow η to change during iterations. In this paper, we introduce a new dynamic adaption mechanism for the parameter η in each cluster and apply it into SP1M. The resultant algorithm, called the Sequential Possibilistic One-Means with Dynamic Eta (SP1M-DE) is shown to provide superior performance over PCM, SP1M, and SP1M-AE in determining correct clustering results.