Self-adaptive kernel K-means algorithm based on the shuffled frog leaping algorithm
文献类型：期刊论文
| 作者 | Fan, Shuyan2; Ding, Shifei1,2; Xue, Yu3 |
| 刊名 | SOFT COMPUTING
 |
| 出版日期 | 2018-02-01 |
| 卷号 | 22期号:3页码:861-872 |
| 关键词 | Kernel K-means Shuffled frog leaping algorithm Clustering validity index Clustering analysis |
| ISSN号 | 1432-7643 |
| DOI | 10.1007/s00500-016-2389-2 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000423704100014 |
| 出版者 | SPRINGER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/5610]  |
| 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.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100090, Peoples R China 2.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, 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. |
入库方式： OAI收割
来源：计算技术研究所
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