基于多扰动的局部自适应软子空间聚类融合算法

计算机科学 ›› 2014, Vol. 41 ›› Issue (2): 240-244.

基于多扰动的局部自适应软子空间聚类融合算法

王丽娟,郝志峰,蔡瑞初,温雯

华南理工大学计算机科学与工程学院广州510006;华南理工大学计算机科学与工程学院广州510006;广东工业大学计算机学院广州510006;广东工业大学计算机学院广州510006

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(61070033,8,61202269),广东省自然科学基金(S2011040004804),广东省科技计划项目(2010B050400011),软件新技术国家重点实验室开放课题(KFKT2011B19),广东高校优秀青年创新人才培育项目(LYM11060),广州市科技计划项目(12C42111607,1),番禺区科技计划项目(2012-Z-03-67)资助

Multiple Local Adaptive Soft Subspace Clustering Ensemble Based on Multimodal Perturbation

WANG Li-juan,HAO Zhi-feng,CAI Rui-chu and WEN Wen

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

摘要/Abstract

摘要： 提出基于随机初始化、参数扰动和特征子集映射的多扰动的局部自适应软子空间聚类(LAC)融合算法(MLACE)。MLACE具有以下特点:(i)多扰动融合:从初始化、参数和特征子集等不同侧面,探测数据内部结构,使之相互融合,从而达到改善聚类正确性的目的；(ii)融合信息提升:根据LAC算法输出的子空间权重矩阵,定义数据属于每一类的概率,形成提升的融合信息；(iii)融合一致性函数改进:融合信息的形式由0/1二值信息转换成[0,1]实值信息,因此,一致性函数采用了性能较优的实数值融合算法Fast global K-means来进一步改善融合正确性。实验选取2个仿真数据库和5个UCI数据库测试MLACE的聚类正确性,实验结果表明,MLACE聚类正确性优于K-means、LAC、基于参数扰动LAC融合算法(P-MLACE)。

关键词: 聚类融合,软子空间聚类,局部自适应软子空间聚类,多扰动中图法分类号TP181文献标识码A

Abstract: This paper proposed multiple local adaptive soft subspace clustering (LAC) ensemble (MLACE) based on multimodal perturbation．There are three merits in the proposed MLACE．Firstly,MLACE combines diversity and complement decisions generated by random initialization,parameter perturbation and feature subspace projection,so as to improve the accuracy of clustering．Secondly,the clustering ensemble information is refined.The probability of each instance belonging to all clusters is defined according to the subspace weight matrix from LAC．Thirdly,because the clustering ensemble information is refined from 0/1binary value into [0,1]real value,the consensus function in clustering ensemble can adopt real valued clustering ensemble method Fast global K means,which can further improve the accuracy of clustering ensemble．Two synthetic datasets and five UCI datasets were chosen to evaluate the accuracy of MLACE．The experiment results show that MLACE is more accurate than K-means,LAC,Multiple LAC clustering ensemble based on parameter perturbation (P-MLACE)．

Key words: Clustering ensemble,Soft subspace clustering,Local adaptive soft subspace clustering,Multimodal perturbation

王丽娟,郝志峰,蔡瑞初,温雯. 基于多扰动的局部自适应软子空间聚类融合算法[J]. 计算机科学, 2014, 41(2): 240-244. https://doi.org/

WANG Li-juan,HAO Zhi-feng,CAI Rui-chu and WEN Wen. Multiple Local Adaptive Soft Subspace Clustering Ensemble Based on Multimodal Perturbation[J]. Computer Science, 2014, 41(2): 240-244. https://doi.org/

参考文献

[1] Kriegel H P,Kroger P,Zimek A.Clustering High-Dimensional Data:A Survey on Subspace Clustering,Pattern-Based Clustering,and Correlation Clustering [J]．ACM Transactions on Knowledge Discovery from Data,2009,3(1):1-58
[2] Parsons L,Haque E,Liu H.Subspace Clustering for High Dimensional Data:A Review [J]．ACM SIGKDD Explorations New-sletter-Special issue on learning from imbalanaced datasets,2004,6(1):90-105
[3] Huang J Z.Automated variable weighting in K-means type clustering [J]．IEEE Trans．on Pattern Analysis and Machine Intelligence,2005,27(5):657-668
[4] Gan G,Wu J.A convergence theorem for the fuzzy subspaceclustering (FSC) algorithm [J]．Pattern Recognition,2008,41(6):1939-1947
[5] Domeniconi C.Locally adaptive metrics fore clustering high dimensional data [J]．Data mining knowledge discovery,2007,14:63-97
[6] Jing L P.An entropy weighting K-means algorithm for subspace clustering of high dimensional sparse data [J]．IEEE Trans．on Knowledge and Data Engineering,2007,19(8):1026-1041

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed