Hyperspherical possibilistic fuzzy c-means for high-dimensional data clustering

A possibilistic fuzzy c-means (PFCM)[1] has been proposed for clustering unlabeled data. It is a hybridization of possibilistic c-means (PCM) and fuzzy c-means (FCM), therefore it has been shown that PFCM is able to solve the noise sensitivity issue in FCM, and at the same time it helps to avoid coincident clusters problem in PCM with some numerical examples in low-dimensional data sets. In this paper, we conduct further evaluation of PFCM for high-dimensional data and proposed a revised version of PFCM called Hyperspherical PFCM (HPFCM). Modifications have been made in the original PFCM objective function, so that cosine similarity measure could be incorporated in the approach. We apply both the original and revised approaches on six large benchmark data sets, and compare their performance with some of the traditional and recent clustering algorithms for automatic document categorization. Our analytical as well as experimental study show HPFCM is promising for handling complex high dimensional data sets and achieves more stable performance. On the other hand, the remaining problem of PFCM approach is also discussed.