Self-adaptive kernel K-means algorithm based on the shuffled frog leaping algorithm

doi:10.1007/s00500-016-2389-2

	Self-adaptive kernel K-means algorithm based on the shuffled frog leaping algorithm
	Fan, Shuyan 1; Ding, Shifei 1,2; Xue, Yu 3
	2018-02-01
发表期刊	SOFT COMPUTING
ISSN	1432-7643
卷号	22 期号:3 页码:861-872
摘要	Kernel K-means can handle nonlinearly separate datasets by mapping the input datasets into a high-dimensional feature space. The kernel matrix reflects the inner structure of data, so it is a key to construct an appropriate kernel matrix. However, many kernel-based methods need to be set kernel parameter artificially in advance. It is difficult to set an appropriate kernel parameter for each dataset artificially, which limits the performance of the kernel K-means algorithm to some extent. It is necessary to design a method which can adjust the kernel parameter automatically according to the data structure. In addition, the number of clusters also needs to be set. To overcome these challenges, this paper proposed a self-adaptive kernel K-means based on the shuffled frog leaping algorithm, which regard the kernel parameter and the number of clusters as the position information of the frog. We designed a clustering validity index named Between-Within Proportion suitable for the kernel space (KBWP) by modifying the clustering validity index Between-Within Proportion (BWP). Treat KBWP as fitness in the shuffled frog leaping algorithm, and then do local and global optimization until the max iterations. The kernel parameter and the number of clusters corresponding to the maximum fitness are optimal. We experimentally verify our algorithm on artificial datasets and real datasets. Experimental results demonstrate the effectiveness and good performance of the proposed algorithm.
关键词	Kernel K-means Shuffled frog leaping algorithm Clustering validity index Clustering analysis
DOI	10.1007/s00500-016-2389-2
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61379101] ; National Natural Science Foundation of China[61672522] ; National Basic Research Program of China[2013CB329502] ; Priority Academic Program Development of Jiangsu Higer Education Institutions (PAPD) ; Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET)
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:000423704100014
出版者	SPRINGER
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/5611
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ding, Shifei
作者单位	1.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100090, Peoples R China 3.Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Fan, Shuyan,Ding, Shifei,Xue, Yu. Self-adaptive kernel K-means algorithm based on the shuffled frog leaping algorithm[J]. SOFT COMPUTING,2018,22(3):861-872.
APA	Fan, Shuyan,Ding, Shifei,&Xue, Yu.(2018).Self-adaptive kernel K-means algorithm based on the shuffled frog leaping algorithm.SOFT COMPUTING,22(3),861-872.
MLA	Fan, Shuyan,et al."Self-adaptive kernel K-means algorithm based on the shuffled frog leaping algorithm".SOFT COMPUTING 22.3(2018):861-872.