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On Using Genetic Algorithm for Initialising Semi-supervised Fuzzy c-Means Clustering

Lai, Daphne Teck Ching; Garibaldi, Jonathan M.

Authors

Daphne Teck Ching Lai



Abstract

In a previous work, suitable initialisation techniques were incorporated with semi-supervised Fuzzy c-Means clustering (ssFCM) to improve clustering results on a trial and error basis. In this work, we present a single fully-automatic version of an existing semi-supervised Fuzzy c-means clustering framework which uses genetically-modified prototypes (ssFCMGA). Initial prototypes are generated by GA to initialise the ssFCM algorithm without experimentation of different initialisation techniques. The framework is tested on a real, biomedical dataset NTBC and on the Arrhythmia UCI dataset, using varying amounts of labelled data from 10% to 60% of the total data patterns. Different ssFCM threshold values and fitness functions for ssFCMGA are also investigated (sGAs). We used accuracy and NMI to measure class-label agreement and internal measures WSS, BSS, CH, CWB, DB and DU to evaluate cluster quality of the clustering algorithms. Results are compared with those produced by the existing ssFCM. While ssFCMGA and sGAs produced slightly lower agreement level than ssFCM with known class labels based on accuracy and NMI, the other six measurements showed improvement in the results in terms of compactness and well-separatedness (cluster quality), particularly when labelled data are low at 10%. Furthermore, the cluster quality are shown to further improve using ssFCMGA with a more complex fitness function (sGA2). This demonstrates the application of GA in ssFCM improves cluster quality without exploration of different initialisation techniques.

Citation

Lai, D. T. C., & Garibaldi, J. M. (2017). On Using Genetic Algorithm for Initialising Semi-supervised Fuzzy c-Means Clustering. Advances in Intelligent Systems and Computing, 532, 3-14. https://doi.org/10.1007/978-3-319-48517-1_1

Journal Article Type Article
Acceptance Date Sep 1, 2016
Online Publication Date Oct 21, 2016
Publication Date 2017
Deposit Date Nov 2, 2016
Publicly Available Date Mar 28, 2024
Journal Advances in Intelligent Systems and Computing
Electronic ISSN 2194-5357
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 532
Pages 3-14
Series Title Advances in Intelligent Systems and Computing
Book Title Computational intelligence in information systems
ISBN 9783319485164
DOI https://doi.org/10.1007/978-3-319-48517-1_1
Keywords semi-supervised, genetic algorithms, fuzzy clustering
Public URL https://nottingham-repository.worktribe.com/output/822290
Publisher URL http://www.springer.com/gp/book/9783319485164
Additional Information Proceedings of the Computational Intelligence in Information Systems Conference (CIIS 2016), held in Brunei, November 18–20, 2016.