[1]HUANG Dong,WANG Changdong,LAI Jianhuang,et al.Clustering ensemble by decision weighting[J].CAAI Transactions on Intelligent Systems,2016,11(3):418-425.[doi:10.11992/tis.201603030]
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Clustering ensemble by decision weighting

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 3 Page number: 418-425 Column: 学术论文—机器学习 Public date: 2016-06-25
Title:
Clustering ensemble by decision weighting
Author(s):
HUANG Dong1;  WANG Changdong2; 3;  LAI Jianhuang2; 3;  LIANG Yun1;  BIAN Shan1;  CHEN Yu1
1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510640, China;
2. School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China;
3. Guangdong Key Laboratory of Information Security Tec
Keywords:
clustering; clustering ensemble; decision weighting; bipartite graph formulation; graph partitioning; base clustering; credit sharing; weighted clustering ensemble
CLC:
TP18
DOI:
10.11992/tis.201603030
Abstract:
The clustering ensemble technique aims to combine multiple base clusterings to achieve better and more robust clustering results.To evaluate the reliability of the base clusterings and weight them accordingly, in this paper, we propose a new clustering ensemble approach based on a bipartite graph formulation and decision weighting strategy. Each base clustering is treated as a bag of decisions, and is assigned one unit of credit. This credit is shared (divided) by all the decisions in one clustering. Using the credit sharing concept, we propose weighting the decisions in the base clusterings with regard to the credit they have. Then, the clustering ensemble problem is formulated into a bipartite graph model that incorporates the decision weights, and the final clustering is obtained by rapidly partitioning the bipartite graph. Experimental results have demonstrated the superiority of the proposed algorithm in terms of both effectiveness and efficiency.
References:
[1] STREHL A, GHOSH J. Cluster ensembles-a knowledge reuse framework for combining multiple partitions[J]. The journal of machine learning research, 2003, 3(3): 583-617.
[2] CRISTOFOR D, SIMOVICI D. Finding median partitions using information-theoretical-based genetic algorithms[J]. Journal of universal computer science, 2002, 8(2): 153-172.
[3] FERN X Z, BRODLEY C E. Solving cluster ensemble problems by bipartite graph partitioning[C]//Proceedings of the 21st International Conference on Machine Learning. New York, NY, USA, 2004.
[4] FRED A L N, JAIN A K. Combining multiple clusterings using evidence accumulation[J]. IEEE transactions on pattern analysis and machine intelligence, 2005, 27(6): 835-850.
[5] WANG Xi, YANG Chunyu, ZHOU Jie. Clustering aggregation by probability accumulation[J]. Pattern recognition, 2009, 42(5): 668-675.
[6] SINGH V, MUKHERJEE L, PENG Jiming, et al. Ensemble clustering using semidefinite programming with applications[J]. Machine learning, 2010, 79(1/2): 177-200.
[7] HUANG Dong, LAI Jianhuang, WANG Changdong. Exploiting the wisdom of crowd: a multi-granularity approach to clustering ensemble[C]//Proceedings of the 4th International Conference on Intelligence Science and Big Data Engineering. Beijing, China, 2013: 112-119.
[8] HUANG Dong, LAI Jianhuang, WANG Changdong. Combining multiple clusterings via crowd agreement estimation and multi-granularity link analysis[J]. Neurocomputing, 2015, 170: 240-250.
[9] HUANG Dong, LAI Jianhuang, WANG Changdong. Ensemble clustering using factor graph[J]. Pattern recognition, 2016, 50: 131-142.
[10] HUANG Dong, LAI Jianhuang, WANG Changdong. Robust ensemble clustering using probability trajectories[J]. IEEE transactions on knowledge and data engineering, 2016, 28(5): 1312-1326.
[11] LI Tao, DING C. Weighted consensus clustering[C]//Proceedings of the 2008 SIAM International Conference on Data mining. Auckland, New Zealand, 2008: 798-809.
[12] KARYPIS G, KUMAR V. Multilevel k-way partitioning scheme for irregular graphs[J]. Journal of parallel and distributed computing, 1998, 48(1): 96-129.
[13] NG A Y, JORDAN M I, WEISS Y. On spectral clustering: Analysis and an algorithm[C]//Advances in Neural Information Processing Systems. Vancouver, Canada, 2001.
[14] TOPCHY A, JAIN A K, PUNCH W. Clustering ensembles: models of consensus and weak partitions[J]. IEEE transactions on pattern analysis and machine intelligence, 2005, 27(12): 1866-1881.
[15] VEGA-PONS S, CORREA-MORRIS J, RUIZ-SHULCLOPER J. Weighted partition consensus via kernels[J]. Pattern recognition, 2010, 43(8): 2712-2724.
[16] VEGA-PONS S, RUIZ-SHULCLOPER J, GUERRA-GANDóN A. Weighted association based methods for the combination of heterogeneous partitions[J]. Pattern recognition letters, 2011, 32(16): 2163-2170.
[17] 徐森, 周天, 于化龙, 等. 一种基于矩阵低秩近似的聚类集成算法[J]. 电子学报, 2013, 41(6): 1219-1224.XU Sen, ZHOU Tian, YU Hualong, et al. Matrix low rank approximation-based cluster ensemble algorithm[J]. Acta electronica sinica, 2013, 41(6): 1219-1224.
[18] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[19] LI Zhenguo, WU Xiaoming, CHANG S F. Segmentation using superpixels: a bipartite graph partitioning approach[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA, 2012: 789-796.
[20] BACHE K, LICHMAN M. UCI machine learning repository[EB/OL]. (2013-04-04). http://archive.ics.uci.edu/ml.
[21] IAM-ON N, BOONGOEN T, GARRETT S. Refining pairwise similarity matrix for cluster ensemble problem with cluster relations[C]//Proceedings of the 11th International Conference on Discovery Science. Budapest, Hungary, 2008: 222-233.
[22] IAM-ON N, BOONGOEN T, GARRETT S, et al. A link-based approach to the cluster ensemble problem[J]. IEEE transactions on pattern analysis and machine intelligence, 2011, 33(12): 2396-2409.
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