research-article

Combining multiple clusterings via crowd agreement estimation and multi-granularity link analysis

Authors:

Jian-Huang Lai,

Chang-Dong WangAuthors Info & Claims

Neurocomputing, Volume 170, Issue C

Pages 240 - 250

https://doi.org/10.1016/j.neucom.2014.05.094

Published: 25 December 2015 Publication History

Abstract

The clustering ensemble technique aims to combine multiple clusterings into a probably better and more robust clustering and has been receiving an increasing attention in recent years. There are mainly two aspects of limitations in the existing clustering ensemble approaches. Firstly, many approaches lack the ability to weight the base clusterings without access to the original data and can be affected significantly by the low-quality, or even ill clusterings. Secondly, they generally focus on the instance level or cluster level in the ensemble system and fail to integrate multi-granularity cues into a unified model. To address these two limitations, this paper proposes to solve the clustering ensemble problem via crowd agreement estimation and multi-granularity link analysis. We present the normalized crowd agreement index (NCAI) to evaluate the quality of base clusterings in an unsupervised manner and thus weight the base clusterings in accordance with their clustering validity. To explore the relationship between clusters, the source aware connected triple (SACT) similarity is introduced with regard to their common neighbors and the source reliability. Based on NCAI and multi-granularity information collected among base clusterings, clusters, and data instances, we further propose two novel consensus functions, termed weighted evidence accumulation clustering (WEAC) and graph partitioning with multi-granularity link analysis (GP-MGLA) respectively. The experiments are conducted on eight real-world datasets. The experimental results demonstrate the effectiveness and robustness of the proposed methods.

References

[1]

L. Xu, A. Krzyzak, E. Oja, Rival penalized competitive learning for clustering analysis, RBF net, and curve detection, IEEE Trans. Neural Netw., 4 (1993) 636-649.

Digital Library

[2]

J. Li, S. Ray, B.G. Lindsay, A nonparametric statistical approach to clustering via mode identification, J. Mach. Learn. Res., 8 (2007) 1687-1723.

Digital Library

[3]

M.-L. Zhang, Z.-H. Zhou, Multi-instance clustering with applications to multi-instance prediction, Appl. Intell., 31 (2009) 47-68.

Digital Library

[4]

F. Zhao, L. Jiao, H. Liu, X. Gao, M. Gong, Spectral clustering with eigenvector selection based on entropy ranking, Neurocomputing, 73 (2010) 1704-1717.

Digital Library

[5]

C.-D. Wang, J.-H. Lai, Energy based competitive learning, Neurocomputing, 74 (2011) 2265-2275.

Digital Library

[6]

M. Li, X. C. Lian, J.T. Kwok, B.L. Lu, Time and space efficient spectral clustering via column sampling, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'11), 2011.

[7]

C.-D. Wang, J.-H. Lai, C.Y. Suen, J.-Y. Zhu, Multi-exemplar affinity propagation, IEEE Trans. Pattern Anal. Mach. Intell., 35 (2013) 2223-2237.

Digital Library

[8]

C.-D. Wang, J.-H. Lai, D. Huang, W.-S. Zheng, SVStream, IEEE Trans. Knowl. Data Eng., 25 (2013) 1410-1424.

Digital Library

[9]

C.-D. Wang, J.-H. Lai, Position regularized support vector domain description, Pattern Recognit., 46 (2013) 875-884.

Digital Library

[10]

A.K. Jain, Data clustering, Pattern Recognit. Lett., 31 (2010) 651-666.

Digital Library

[11]

S. Vega-Pons, J. Ruiz-Shulcloper, A survey of clustering ensemble algorithms, Int. J. Pattern Recognit. Artif. Intell., 25 (2011) 337-372.

[12]

A. Strehl, J. Ghosh, Cluster ensembles, J. Mach. Learn. Res., 3 (2003) 583-617.

Digital Library

[13]

X.Z. Fern, C.E. Brodley, Solving cluster ensemble problems by bipartite graph partitioning, in: Proceedings of the International Conference on Machine Learning (ICML'04), 2004.

[14]

A.L.N. Fred, A.K. Jain, Combining multiple clusterings using evidence accumulation, IEEE Trans. Pattern Anal. Mach. Intell., 27 (2005) 835-850.

Digital Library

[15]

A. Topchy, A.K. Jain, W. Punch, Clustering ensembles, IEEE Trans. Pattern Anal. Mach. Intell., 27 (2005) 1866-1881.

Digital Library

[16]

S.T. Hadjitodorov, L.I. Kuncheva, L.P. Todorova, Moderate diversity for better cluster ensembles, Inf. Fusion, 7 (2006) 264-275.

Digital Library

[17]

Y. Li, J. Yu, P. Hao, Z. Li, Clustering ensembles based on normalized edges, in: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'07), 2007.

[18]

N. Iam-On, T. Boongoen, S. Garrett, Refining pairwise similarity matrix for cluster ensemble problem with cluster relations, in: Proceedings of the International Conference on Discovery Science (ICDS'08), 2008.

[19]

C. Domeniconi, M. Al-Razgan, Weighted cluster ensembles, ACM Trans. Knowl. Discov. Data, 2 (2009) 1-40.

Digital Library

[20]

X. Wang, C. Yang, J. Zhou, Clustering aggregation by probability accumulation, Pattern Recognit., 42 (2009) 668-675.

Digital Library

[21]

S. Mimaroglu, E. Erdil, Combining multiple clusterings using similarity graph, Pattern Recognit., 44 (2011) 694-703.

Digital Library

[22]

N. Iam-On, T. Boongoen, S. Garrett, C. Price, A link-based approach to the cluster ensemble problem, IEEE Trans. Pattern Anal. Mach. Intell., 33 (2011) 2396-2409.

Digital Library

[23]

J. Yi, T. Yang, R. Jin, A.K. Jain, Robust ensemble clustering by matrix completion, in: Proceedings of the IEEE International Conference on Data Mining (ICDM'12), 2012.

Digital Library

[24]

L. Franek, X. Jiang, Ensemble clustering by means of clustering embedding in vector spaces, Pattern Recognit., 47 (2014) 833-842.

Digital Library

[25]

D. Huang, J.-H. Lai, C.-D. Wang, Exploiting the wisdom of crowd: a multi-granularity approach to clustering ensemble, in: Proceedings of the International Conference on Intelligence Science and Big Data Engineering (IScIDE'13), 2013.

[26]

D. Cristofor, D. Simovici, Finding median partitions using information-theoretical-based genetic algorithms, J. Univers. Comput. Sci., 8 (2002) 153-172.

[27]

E. Weiszfeld, F. Plastria, On the point for which the sum of the distances to n given points is minimum, Ann. Oper. Res., 167 (2009) 7-41.

[28]

S. Vega-Pons, J. Correa-Morris, J. Ruiz-Shulcloper, Weighted partition consensus via kernels, Pattern Recognit., 43 (2010) 2712-2724.

Digital Library

[29]

S. Vega-Pons, J. Ruiz-Shulcloper, A. Guerra-Gandón, Weighted association based methods for the combination of heterogeneous partitions, Pattern Recognit. Lett., 32 (2011) 2163-2170.

Digital Library

[30]

T. Li, C. Ding, Weighted consensus clustering, in: Proceedings of the SIAM International Conference on Data Mining (SDM'08), 2008.

[31]

X.Z. Fern, W. Lin, Cluster ensemble selection, Stat. Anal. Data Min., 1 (2008) 128-141.

Digital Library

[32]

S. Wu, T.W.S. Chow, Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density, Pattern Recognit., 37 (2004) 175-188.

[33]

K. Faceli, M.C.P. de Souto, D.S.A. de Araújo, A.C.P.L.F. de Carvalho, Multi objective clustering ensemble for gene expression data analysis, Neurocomputing, 72 (2009) 2763-2774.

Digital Library

[34]

N. Li, L. J. Latecki, Clustering aggregation as maximum-weight independent set, in: Advances in Neural Information Processing Systems (NIPS'12), 2012.

[35]

J. Surowiecki, The Wisdom of Crowds: Why The Many Are Smarter Than The Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, Anchor Books, New York, 2004.

[36]

M. Levandowsky, D. Winter, Distance between sets, Nature, 234 (1971) 34-35.

[37]

Z. Li, X.-M. Wu, S.-F. Chang, Segmentation using superpixels: a bipartite graph partitioning approach, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'12), 2012.

Digital Library

[38]

K. Bache, M. Lichman, UCI Machine Learning Repository, 2013.

[39]

D. Huang, J.-H. Lai, C.-D. Wang, Incremental support vector clustering with outlier detection, in: Proceedings of the International Conference on Pattern Recognition (ICPR'12), 2012.

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Combining multiple clusterings via crowd agreement estimation and multi-granularity link analysis
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image Neurocomputing

Neurocomputing Volume 170, Issue C

December 2015

466 pages

ISSN:0925-2312

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Copyright © Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 25 December 2015

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https://dl.acm.org/doi/10.1016/j.eswa.2023.121672
Wu ZCai MXu FLi Q(2024)PCS-granularity weighted ensemble clustering via Co-association matrixApplied Intelligence10.1007/s10489-024-05368-354:5(3884-3901)Online publication date: 1-Mar-2024
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Yue GDeng AQu YCui HLiu J(2023)Fuzzy-Rough induced spectral ensemble clusteringJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22389745:1(1757-1774)Online publication date: 1-Jan-2023
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