An enhanced Kernel Fuzzy C-Means Algorithm based on bio-inspired computing methods

	An enhanced Kernel Fuzzy C-Means Algorithm based on bio-inspired computing methods
	Liu Y(刘洋); Hu KY(胡琨元); Zhu YL(朱云龙); Chen HN(陈瀚宁)
作者部门	信息服务与智能控制技术研究室
会议名称	International Conference on Electronics, Information Technology and Intellectualization, EITI 2014
会议日期	August 16-17, 2014
会议地点	Shenzhen, China
会议录名称	Electronics, Information Technology and Intellectualization - International Conference on Electronics, Information Technology and Intellectualization, EITI 2014
出版者	Taylor & Francis Group
出版地	London
	2014
页码	115-118
收录类别	EI
EI收录号	20153501217505
产权排序	1
ISBN号	978-1-138-02741-1
关键词	Data Clustering Bio-inspired Computing Optimization Algorithm Kernel Fuzzy C-means Algorithm Artificial Bee Colony
摘要	In data analysis and data mining technique fields, one of the most widely used methods is clustering. Recently, one of the bio-inspired computing optimization algorithms called the Artificial Bee Colony (ABC) algorithm has been introduced, which has many characteristics, such as simple, robust, stochastic global optimization. In this paper, an enhancedKernel Fuzzy C-MeansAlgorithm (KFCM) based on theABC algorithm for data clustering is proposed. Compared with other popular bio-inspired computing optimization algorithms in data clustering, the results proved that the number of iterations is fewer, the convergence speed is faster and there is also a large improvement in the quality of clustering.
语种	英语
文献类型	会议论文
条目标识符	http://ir.sia.cn/handle/173321/20081
专题	信息服务与智能控制技术研究室
作者单位	Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
推荐引用方式 GB/T 7714	Liu Y,Hu KY,Zhu YL,et al. An enhanced Kernel Fuzzy C-Means Algorithm based on bio-inspired computing methods[C]. London:Taylor & Francis Group,2014:115-118.

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