Voting-Merging: An Ensemble Method for Clustering

Dimitriadou, Evgenia; Weingessel, Andreas; Hornik, Kurt

doi:10.1007/3-540-44668-0_31

Cited by 71 publications

(45 citation statements)

References 6 publications

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“…Community matching (consensus clustering) can be done in a hard or a soft way. Hard community matching can be performed by selecting the best matching pair of communities one by one, avoiding conflict with already selected pairs [10,11] or using greedy algorithms like CLUMPP [20]. Soft community matching can be done so as to minimize the distance between the two matrices [27].…”

Section: Related Workmentioning

confidence: 99%

Integrating community matching and outlier detection for mining evolutionary community outliers

Gupta

Gao²,

Sun

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Temporal datasets, in which data evolves continuously, exist in a wide variety of applications, and identifying anomalous or outlying objects from temporal datasets is an important and challenging task. Different from traditional outlier detection, which detects objects that have quite different behavior compared with the other objects, temporal outlier detection tries to identify objects that have different evolutionary behavior compared with other objects. Usually objects form multiple communities, and most of the objects belonging to the same community follow similar patterns of evolution. However, there are some objects which evolve in a very different way relative to other community members, and we define such objects as evolutionary community outliers. This definition represents a novel type of outliers considering both temporal dimension and community patterns. We investigate the problem of identifying evolutionary community outliers given the discovered communities from two snapshots of an evolving dataset. To tackle the challenges of community evolution and outlier detection, we propose an integrated optimization framework which conducts outlier-aware community matching across snapshots and identification of evolutionary outliers in a tightly coupled way. A coordinate descent algorithm is proposed to improve community matching and outlier detection performance iteratively. Experimental results on both synthetic and real datasets show that the proposed approach is highly effective in discovering interesting evolutionary community outliers.

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Section: Related Workmentioning

confidence: 99%

Integrating community matching and outlier detection for mining evolutionary community outliers

Gupta

Gao²,

Sun

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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“…Recently, there has been an emergent interest in studying cluster ensembles to enhance the quality and robustness of data clustering and to accommodate a wider variety of data types and clusters structures [3][4][5][6][7][8][9][10]. Some of the research have relied on using a co-association matrix as a voting medium for finding the combined partitioning.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Model Using Information Theoretic Measures for Cluster Ensembles

Ayad

Basir

Kamel

2004

Lecture Notes in Computer Science

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Abstract. This paper presents a probabilistic model for combining cluster ensembles utilizing information theoretic measures. Starting from a co-association matrix which summarizes the ensemble, we extract a set of association distributions, which are modelled as discrete probability distributions of the object labels, conditional on each data object. The key objectives are, first, to model the associations of neighboring data objects, and second, to allow for the manipulation of the defined probability distributions using statistical and information theoretic means. A Jensen-Shannon Divergence based Clustering Combination (JSDCC) method is proposed. The method selects cluster prototypes from the set of association distributions based on entropy maximization and maximization of the generalized JS divergence among the selected prototypes. The method proceeds by grouping association distributions by minimizing their JS divergences to the selected prototypes. By aggregating the grouped association distributions, we can represent empirical cluster conditional probability distributions of the object labels, for each of the combined clusters. Finally, data objects are assigned to their most likely clusters, and their cluster assignment probabilities are estimated. Experiments are performed to assess the presented method and compare its performance with other alternative co-association based methods.

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“…After overall consistent re-labeling, voting can be applied to determining cluster membership for each data item. Dimitriadou et al [5] proposed a voting/merging procedure that combines clusterings pair-wise and iteratively. The correspondence problem is solved at each iteration and fuzzy membership decisions are accumulated during the course of merging.…”

Section: Related Workmentioning

confidence: 99%

“…In Step 5 of Algorithm 1, D is computed based on (10) and is a very small positive number used to avoid dividing by 0. for h = 1 to r do 4: while convergence criterion of S (h) is not satisfied do 5:…”

Section: Definition 42 Given R Membership Matrices Mmentioning

confidence: 99%