DocumentCode
2561885
Title
A new clustering validity function for the Fuzzy C-means algorithm
Author
Wang, Jiesheng
Author_Institution
Sch. of Electron. & Inf. Eng., Liaoning Univ. of Sci.&Technol., Anshan
fYear
2008
fDate
2-4 July 2008
Firstpage
2477
Lastpage
2480
Abstract
Fuzzy c-means (FCM) clustering algorithm is the unsupervised extraction of groups from an unlabelled data set with no prior knowledge of the underlying data structure. However there is a major limitation that exists in this method. A predefined number of clusters must be given in advance. In this paper, we propose a new validity index to deal with this situation. The performance evaluation of the proposed cluster validity index compares favorably with that of several validity functions and shows the effectiveness.
Keywords
data structures; fuzzy set theory; pattern clustering; clustering validity function; data structure; fuzzy c-means algorithm; performance evaluation; unlabelled data set; validity index; Clustering algorithms; Data engineering; Data mining; Data structures; Fuzzy sets; Knowledge engineering; Partitioning algorithms; Virtual colonoscopy; Clustering Validity Function; Fuzzy C-means Clustering; Fuzzy Partition;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference, 2008. CCDC 2008. Chinese
Conference_Location
Yantai, Shandong
Print_ISBN
978-1-4244-1733-9
Electronic_ISBN
978-1-4244-1734-6
Type
conf
DOI
10.1109/CCDC.2008.4597770
Filename
4597770
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