Title :
A novel FCM clustering algorithm for interval-valued data and fuzzy-valued data
Author :
Xinbo, Gao ; Hongbing, Ji ; Weixin, Xie
Author_Institution :
Sch. of Electron. Eng., Xidian Univ., Xi´´an, China
Abstract :
It is well known that fuzzy c-means (FCM) algorithm is one of the most popular methods of cluster analysis. However, the traditional FCM algorithm does not work for the interval-valued data and fuzzy-valued data. To this end, a feature mapping method is proposed to preprocess these special type data, and then the traditional FCM algorithm can also be employed to analyze the interval-valued and fuzzy-valued data. Therefore, a novel FCM clustering algorithm is formed for interval-valued data and fuzzy-valued data. The experimental result demonstrates its effectiveness
Keywords :
fuzzy set theory; pattern clustering; FCM clustering algorithm; cluster analysis; feature mapping method; fuzzy c-means algorithm; fuzzy-valued data; interval-valued data; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Computer vision; Fuzzy control; Fuzzy sets; Interference; Pattern classification; Pattern recognition; Random variables;
Conference_Titel :
Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-5747-7
DOI :
10.1109/ICOSP.2000.893395
