A new K-means algorithm for community structures detection based on Fuzzy clustering

被引：0
|
作者：
Liu, Qun [1 ]
Peng, Zhiming [1 ]
Gao, Yi [1 ]
Liu, Qian [1 ]
机构：
[1] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Chongqing, Peoples R China
来源：
2012 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING (GRC 2012) | 2012年
关键词：
Social network; K-means algorithm; Community structure; Fuzzy C-Means clustering;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Finding community structures from networks is one of the most popular research areas in recent years. Because of the shortcoming of FCM, for example, its results depend on the initial center node and need to specify the community number, based on the fuzzy theory, an improved FCM algorithm(NKFCM) is proposed, which can get the number of communities and the community centers automatically. NKFCM is used to find the communities of network. The experiments in real networks show that this method can get better results.
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页数：5
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