Adaptive Spectral Clustering Ensemble Selection via Resampling and Population-Based Incremental Learning Algorithm  

作　　者：XU Yuanchun JIA Jianhua 

机构地区：[1]School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen 333002, Jiangxi, China

出　　处：《Wuhan University Journal of Natural Sciences》2011年第3期228-236,共9页武汉大学学报（自然科学英文版）

基　　金：Supported by the National Natural Science Foundation of China (60661003);the Research Project Department of Education of Jiangxi Province (GJJ10566)

摘　　要：In this paper, we explore a novel ensemble method for spectral clustering. In contrast to the traditional clustering ensemble methods that combine all the obtained clustering results, we propose the adaptive spectral clustering ensemble method to achieve a better clustering solution. This method can adaptively assess the number of the component members, which is not owned by many other algorithms. The component clusterings of the ensemble system are generated by spectral clustering （SC） which bears some good characteristics to engender the diverse committees. The selection process works by evaluating the generated component spectral clustering through resampling technique and population-based incremental learning algorithm （PBIL）. Experimental results on UCI datasets demonstrate that the proposed algorithm can achieve better results compared with traditional clustering ensemble methods, especially when the number of component clusterings is large.In this paper, we explore a novel ensemble method for spectral clustering. In contrast to the traditional clustering ensemble methods that combine all the obtained clustering results, we propose the adaptive spectral clustering ensemble method to achieve a better clustering solution. This method can adaptively assess the number of the component members, which is not owned by many other algorithms. The component clusterings of the ensemble system are generated by spectral clustering （SC） which bears some good characteristics to engender the diverse committees. The selection process works by evaluating the generated component spectral clustering through resampling technique and population-based incremental learning algorithm （PBIL）. Experimental results on UCI datasets demonstrate that the proposed algorithm can achieve better results compared with traditional clustering ensemble methods, especially when the number of component clusterings is large.

关 键 词：spectral clustering clustering ensemble selective ensemble RESAMPLING population-based incremental learning algorithm （PBIL） data clustering 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]