DocumentCode :
2375806
Title :
A new validity measure for a correlation-based fuzzy c-means clustering algorithm
Author :
Zhang, Mingrui ; Zhang, Wei ; Sicotte, Hugues ; Yang, Ping
Author_Institution :
Comput. Sci. Dept., Winona State Univ., Winona, MN, USA
fYear :
2009
fDate :
3-6 Sept. 2009
Firstpage :
3865
Lastpage :
3868
Abstract :
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.
Keywords :
biology computing; fuzzy set theory; genetics; pattern clustering; Pearson correlation; fuzzy c-means clustering; fuzzy partition coefficient; fuzzy validity index; unsupervised clustering; Algorithms; Cluster Analysis; Computational Biology; Fuzzy Logic; Gene Expression Profiling; Gene Expression Regulation; Genes, Fungal; Humans; Lung Neoplasms; Models, Statistical; Oligonucleotide Array Sequence Analysis; Pattern Recognition, Automated; Sequence Alignment; Software; Time Factors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE
Conference_Location :
Minneapolis, MN
ISSN :
1557-170X
Print_ISBN :
978-1-4244-3296-7
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2009.5332582
Filename :
5332582
Link To Document :
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