Paper Info
Title
Improved FCM Algorithm Based on K-Means and Granular Computing.
Abstract
The fuzzy clustering algorithm has been widely used in the research area and production and life. However, the conventional fuzzy algorithms have a disadvantage of high computational complexity. This article proposes an improved fuzzy C-means (FCM) algorithm based on K-means and principle of granularity. This algorithm is aiming at solving the problems of optimal number of clusters and sensitivity to the data initialization in the conventional FCM methods. The initialization stage of the K-medoid cluster, which is different from others, has a strong representation and is capable of detecting data with different sizes. Meanwhile, through the combination of the granular computing and FCM, the optimal number of clusters is obtained by choosing accurate validity functions. Finally, the detailed clustering process of the proposed algorithm is presented, and its performance is validated by simulation tests. The test results show that the proposed improved FCM algorithm has enhanced clustering performance in the computational complexity, running time, cluster effectiveness compared with the existing FCM algorithms.
Year
DOI
Venue
2015
10.1515/jisys-2014-0119
JOURNAL OF INTELLIGENT SYSTEMS
Keywords
DocType
Volume
Fuzzy cluster,K-means,FCM algorithm,principle of granularity
Journal
24
Issue
ISSN
Citations 
2
0334-1860
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Wei Jia Lu100.34
Zhuang-zhi Yan2148.28