Hierarchical Possibilistic Clustering

This is a good paper for reference for computer science related branch on "hierarchical possibilistic clustering". In this paper we propose to combine two clustering approaches, namely fuzzy and possibilistic c-means. While fuzzy c-means algorithm finds suitable clusters for groups of data points, obtained memberships of data, however, encounters a major deficiency caused by misinterpretation of membership values of data points. Therefore, membership values cannot correctly interpret compatibility or degree to which data points belong to clusters. You can also Subscribe to FINAL YEAR PROJECT'S by Email for more such projects and seminar.


As a result, noisy data will be misinterpreted by incorrect memberships assigned, as sum of memberships of each noisy data to all clusters is constrained to be equal to 1. To overcome this, a possibilistic approach has been proposed which removes this constraint. It has, however, caused another shortcoming as cluster centers converge to an identical point. Therefore, possibilities cannot correctly interpret the degrees of compatibilities. Thanks to author, use this only for your reference and study work.

Author:-Mehdi Salkhordeh Haghighi1, Hadi Sadoghi Yazdi2, AbedinVahedian3
Source:- IJCTE

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