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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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