Title :
A new clustering validity function for the Fuzzy C-means algorithm
Author_Institution :
Sch. of Electron. & Inf. Eng., Liaoning Univ. of Sci.&Technol., Anshan
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;
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
DOI :
10.1109/CCDC.2008.4597770
