DocumentCode
3309140
Title
A new validation index for determining the number of clusters in a data set
Author
Sun, Haojun ; Wang, Shengrui ; Jiang, Qingshan
Author_Institution
Dept. of Math. & Comput. Sci., Sherbrooke Univ., Que., Canada
Volume
3
fYear
2001
fDate
2001
Firstpage
1852
Abstract
Clustering analysis plays an important role in solving practical problems in such domains as data mining in large databases. In this paper, we are interested in fuzzy c-means (FCM) based algorithms. The main purpose is to design an effective validity function to measure the result of clustering and detecting the best number of clusters for a given data set in practical applications. After a review of the relevant literature, we present the new validity function. Experimental results and comparisons will be given to illustrate the performance of the new validity function
Keywords
data mining; neural nets; pattern clustering; FCM based algorithms; clustering analysis; data mining; data set clusters; effective validity function; fuzzy c-means based algorithms; validation index; Clustering algorithms; Data analysis; Data mining; Image databases; Image processing; Partitioning algorithms; Pattern recognition; Performance analysis; Phase change materials; Sun;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.938445
Filename
938445
Link To Document