An Improved Fuzzy C-Means Algorithm Based on MapReduce

被引：0
|
作者：
Yu, Qing [1 ]
Ding, Zhimin [1 ]
机构：
[1] Tianjin Univ Technol, Tianjin Key Lab Intelligence Comp & Network Secur, Tianjin, Peoples R China
来源：
2015 8TH INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND INFORMATICS (BMEI) | 2015年
关键词：
Clustering; The Fuzzy C-means algorithm; a Max-min Principle; The Canopy algorithm; MapReduce;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
In order to solve the problem that the Fuzzy C-Means algorithm is sensitive to the initial clustering center, we use the Canopy algorithm to carry out the quick and rough clustering. At the same time, to avoid the blindness of the Canopy algorithm, we put forward an improved Canopy-FCM algorithm based on a max-min principle. In allusion to the problem that the FCM algorithm has high time complexity, this article use the parallel computing frame of MapReduce to design and realize the improved Canopy-FCM algorithm. Experimental results show: the improved Canopy-FCM algorithm based on MapReduce has better clustering quality and running speed than the Canopy-FCM and the FCM algorithm based on MapReduce, and the improved Canopy-FCM algorithm based on Hadoop has better speed-up ratio than the Canopy-FCM based on the Standalone mode.
引用
收藏
页码：634 / 638
页数：5
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