An Unsupervised Alternating Clustering Method

M.-S. Yang and K.-L. Wu (Taiwan)


Fuzzy c-means (FCM); Possibilistic c-means (PCM); Pos sibilistic clustering algorithm (PCA); Validity index.


In this paper, we propose an alternating clustering, called a possibilistic clustering algorithm (PCA), which is based on the FCM objective function, the partition coefficient (PC) and partition entropy (PE) validity indexes. The resulting membership becomes the exponential function, so that it is robust to noise and outliers. To validate the clustering results obtained through PCA, we generalized the validity indexes of FCM. This generalization makes each validity index workable in both fuzzy and possibilistic clustering models. Some numerical examples and real data are im plemented on the basis of the proposed PCA. These results show its effectiveness and accuracy.

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