Abstract Clustering has been widely used 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 in other clusters. An Improved possibilistic clustering algorithm was developed based on the conventional possibilistic cmeans (PCM) to obtain better quality... Clustering has been widely used 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 in other clusters. An Improved possibilistic clustering algorithm was developed based on the conventional possibilistic cmeans (PCM) to obtain better quality clustering result. The new approach uses a parameter â based on the factor of separation between a point and a cluster. This coefficient is fed into the objective function; it defines the relative importance of typicality values in the algorithm. Read More Read Less