A new validity measure for a correlation-based fuzzy c-means clustering algorithm.
One of the major challenges in unsupervised clustering is the lack of consistent means for assessing the quality of clusters. In this paper, we evaluate several validity measures in fuzzy clustering and develop a new measure for a fuzzy c-means algorithm which uses a Pearson correlation in its distance metrics. The measure is designed with within-cluster sum of square, and makes use of fuzzy memberships. In comparing to the existing fuzzy partition coefficient and a fuzzy validity index, this new measure performs consistently across six microarray datasets. The newly developed measure could be used to assess the validity of fuzzy clusters produced by a correlation-based fuzzy c-means clustering algorithm.
Zhang
Mingrui
M
Computer Science Department, Winona State University, MN 55987, USA. mzhang@winona.edu
Zhang
Wei
W
Sicotte
Hugues
H
Yang
Ping
P
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation; D005800:Genes, Fungal; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics / Q000378:metabolism; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D016415:Sequence Alignment / Q000379:methods; D012984:Software; D013997:Time Factors