Abstract
This paper evaluates the performance of 5 previously presented in the literature cluster validity indices for the Fuzzy C-Means (FCM) clustering algorithm. The first two indices, the Fuzzy Partition Coefficient (<i>PC</i>), Fuzzy Partition Entropy Coefficient (<i>PEC</i>) select the number of clusters for which the fuzzy partition is more “crisp-like” or less fuzzy. The other three indices are the Fuzzy Davies-Bouldin Index (<i>FDB</i>), Xie-Beni Index (<i>XB</i>), and the <i>Index I (I)</i> choose the number of clusters which maximizes the inter-cluster separation and minimizes the within cluster scatter. A modification to these three indices is proposed based on the Bhattacharyya distance between clusters. The results show that this modification improves upon the performance of <i>Index I</i>. On the data sets presented on this paper the modifications of indices FDB and XB performed adequately.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
