A study on possibilistic and fuzzy possibilistic C-means clustering algorithms for data clustering

Data clustering is one of the important data mining tasks. It is the process of grouping objects into clusters such that objects in the same clusters are more similar to each other than the objects in different clusters. It has been applied in many fields., including data mining., data analysis., document retrieval., machine learning., pattern recognition., bioinformatics and image analysis. Fuzzy c-means (FCM) algorithm is one of the important clustering techniques. However., it is sensitive to noises and is easily struck at local minima. The possibilistic c-means (PCM) algorithm proposed in the literature solves the noise sensitivity problem of FCM algorithm. However., the performance of PCM depends heavily on the initialization and often deteriorates due to the coincident clustering problem. Fuzzy possibilistic c-means algorithm (FPCM) solves the problems of both FCM and PCM algorithms. In this paper., we studied PCM and FPCM clustering techniques. The algorithms are tested with five real world data sets and randomly generated data set. A brief review of applications of these algorithms is also described.