Implementation of Fuzzy C-Means and Possibilistic C-Means Clustering Algorithms, Cluster Tendency Analysis and Cluster Validation
Machine Learning
2019-05-14 v3 Computer Vision and Pattern Recognition
Abstract
In this paper, several two-dimensional clustering scenarios are given. In those scenarios, soft partitioning clustering algorithms (Fuzzy C-means (FCM) and Possibilistic c-means (PCM)) are applied. Afterward, VAT is used to investigate the clustering tendency visually, and then in order of checking cluster validation, three types of indices (e.g., PC, DI, and DBI) were used. After observing the clustering algorithms, it was evident that each of them has its limitations; however, PCM is more robust to noise than FCM as in case of FCM a noise point has to be considered as a member of any of the cluster.
Keywords
Cite
@article{arxiv.1809.08417,
title = {Implementation of Fuzzy C-Means and Possibilistic C-Means Clustering Algorithms, Cluster Tendency Analysis and Cluster Validation},
author = {Md. Abu Bakr Siddique and Rezoana Bente Arif and Mohammad Mahmudur Rahman Khan and Zahidun Ashrafi},
journal= {arXiv preprint arXiv:1809.08417},
year = {2019}
}
Comments
8 pages, 13 figures, 8 tables