A New Improved Fuzzy Possibilistic C-Means Algorithm Based on Weight Degree

摘要

Clustering (or cluster analysis) has been used widely in pattern recognition, image processing, and data analysis. It aims to organize a collection of data items into clusters, such that items within a cluster are more similar to each other than they are items in the other clusters. An improved fuzzy possibilistic clustering algorithm was developed based on the conventional fuzzy possibilistic c-means (FPCM) to obtain better quality clustering results. Numerical simulations show that the clustering algorithm gives more accurate clustering results than the FCM and FPCM methods.