计算机科学 ›› 2015, Vol. 42 ›› Issue (7): 48-51.doi: 10.11896/j.issn.1002-137X.2015.07.011

• 2014’全国理论计算机科学年会 • 上一篇    下一篇

聚类集成时机的确定

孟晓龙 杨 燕 王红军 肖文超   

  1. 西南交通大学信息科学与技术学院 成都610031
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金(61170111,2),西南交通大学牵引动力国家重点实验室自主研究课题(2012TPL_T15)资助

Occasion Determination of Clustering Ensemble

MENG Xiao-long YANG Yan WANG Hong-jun XIAO Wen-chao   

  • Online:2018-11-14 Published:2018-11-14

摘要: 使用集成学习技术可以提高聚类性能。在实验中发现,当各聚类成员聚类迭代到中后期时进行集成所得的结果会优于其迭代完全停止时进行集成所得的结果。利用集成网络泛化能力的偏差-方差分解理论对聚类集成过程中的上述现象进行解释,将提高集成网络间泛化能力的早期停止准则应用于聚类集成过程,并提出聚类集成时机的概念。对比实验表明,基于早期停止准则的聚类集成得到的结果较好,且更节约聚类集成的时间,为寻求聚类集成的最佳时机提供了可行性建议和方法。

关键词: 聚类集成,集成时机,泛化能力,早期停止准则

Abstract: Ensemble learning technique may improve the clustering performance.In the experiment,we discovered that combining the mid-to-late solutions of cluster members in different initial conditions probably get the better ensemble results than combining the end ones.We used the bias/variance trade-off of generalization ability in ensemble network to explain this phenomenon,applied the early stopping rules to the clustering ensemble and proposed the concept of clustering ensemble occasion.The experimental results show that the performance of clustering ensemble based on the early stopping rules is superior to that based on the end solutions of cluster members,while the former takes less time,thus giving some useful suggestions for seeking the best clustering ensemble occasion.

Key words: Clustering ensemble,Ensemble occasion,Generalization ability,Early stopping rules

[1] Han Jia-wei,Kamber M,Pei J.Data Mining Concepts and Techniques [M].Beijing:China Machine Press,2012
[2] 郭鹏飞,刘万军,林琳,等.结合随机游走和FCM的脑图像分割方法[J].计算机科学,2014,41(7):322-325 Guo Peng-fei,Liu Wang-jun,Lin Lin,et al.Brain Image Segmentation Method Based on FCM and Random Walk[J].Computer Science,2014,41(7):322-325
[3] 吕明磊,刘冬梅,曾智勇.一种改进的K-means聚类算法的图像检索方法[J].计算机科学,2013,40(8):285-288 Lv Ming-lei,Liu Dong-mei,Zeng Zhi-yong.Novel Image Retrieval Method of Improved K-means Clustering Algorithm[J].Computer Science,2013,40(8):285-288
[4] Strehl A,Ghosh J.Cluster ensembles-A knowledge reuse framework for combining partitionings[J].Journal of Machine Lear-ning Research,2002,3:583-617(下转第84页)(上接第51页)
[5] Fred A,Jain A K.Data clustering using evidence accumulation[C]∥Proceedings of the 17th International Conference on Pattern Recognition.2002:276-280
[6] Zhou Z H,Tang W.Cluster ensemble[J].Knowledge-BasedSystems,2006,19(1):77-83
[7] Lu X Y,Yang Y,Wang H J.Selective clustering ensemble based on covariance[C]∥Proceedings of the 11th International Workshop on Multiple Classifier Systems,2013.LNCS,2013,7872:179-189
[8] 罗会兰,孔繁胜,李一啸.聚类集成中的差异性度量研究[J].计算机学报,2007,30(8):1315-1324 Luo Hui-lan,Kong Fan-sheng,Li Yi-xiao.An Analysis of Diversity Measures in Clustering Ensembles[J].Chinese Journal of Computers,2007,30(8):1315-1324
[9] Krogh A,Vedelsby J.Neural network ensembles,cross valida-tion,and active learning[C]∥Proceedings of NIPS94-Neural Information Processing Systems:Natural and Synthetic,1994.Advances in Neural Information Processing Systems 7,MIT Press,1995:231-238
[10] Lodwich A,Rangoni Y,Breuel T.Evaluation of robustness and performance of early stopping rules with multilayer perceptrons[C]∥Proceedings of the 2009 International Joint Conference on Neural Networks.2009:1877-1884
[11] 杨燕,靳蕃,Kamelk M.聚类有效性评价综述[J].计算机应用研究,2008,25(6):1632-1638 Yang Yan,Jin Fan,Kamel M.Survey of clustering evaluation[J].Application Research of Computers,2008,25(6):1632-1638
[12] Pakira M K,Bandyopadhyay S,Maulik U.Validity index forcrisp and fuzzy clusters [J].Pattern Recognition,2004,37:487-501
[13] Brualdi.Introductory Combinatorics,Fifth Edition[M].Beijing:Prentice Hall,2012

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!