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
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;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.938445
