Title :
Improved fuzzy c-means algorithm based on minimum of distance cost function
Author :
Xiaoyun, Wang ; Shujun, Lei
Author_Institution :
Institute of Management Sciences and Information, Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, P.R. China
Abstract :
The traditional fuzzy c-means (FCM) operates when cluster number c is assigned. The value of c makes a great influence on the cluster result. However, the value of cluster number can not be confirmed automatically and needs to be inputted manually, which results in hinders when using the fuzzy c-means. Some researchers have investigated the problem. By combining the concept of distance cost function with the character of fuzzy c-means, this paper improves the FCM algorithm based on new formula of distance cost function. According to calculation of the minimum of modified formula, the optimal cluster number c can be confirmed. The analysis of synthetic and real-world data demonstrate that, improved FCM based on minimum of distance cost function can reach the optimal cluster number.
Keywords :
Algorithm design and analysis; Clustering algorithms; Cost function; Data mining; Equations; Iris; Mathematical model; clustering; data mining; distance cost function; fuzzy c-means;
Conference_Titel :
E -Business and E -Government (ICEE), 2011 International Conference on
Conference_Location :
Shanghai, China
Print_ISBN :
978-1-4244-8691-5
DOI :
10.1109/ICEBEG.2011.5877018
