Combining Multiple Clusterings by Soft Correspondence

Long, Bo; Zhang, Zhongfei; Yu, Philip S.

doi:10.1109/icdm.2005.45

Cited by 27 publications

(34 citation statements)

References 11 publications

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“…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]. In computer vision, community matching has been Table 1: Table of Notations done by using cluster features like position, intensity, shape and average gray-scale difference [23], and degree of match between surrounding clusters [28].…”

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

View full text Add to dashboard Cite

show abstract

“…Many cluster ensemble approaches use a co-association matrix to accumulate the similarity between data points by all given clusterings [14,36]. Other approaches focus on finding the correspondence between cluster labels produced by different clustering systems [26]. In many cases, the numbers of clusters in the final clustering is preset.…”

Section: Clustering Ensemblesmentioning

confidence: 99%

“…The problem of combining multiple clusterings into a single consolidated clustering has been studied extensively under the names of clustering ensembles [36], clustering combination [14,26] or clustering aggregation [16] in recent years. The final clustering is usually the one that agrees the most with the given clusterings.…”

Section: Clustering Ensemblesmentioning

confidence: 99%

Exploiting context analysis for combining multiple entity resolution systems

Chen

Kalashnikov

Mehrotra

2009

Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data

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Entity Resolution (ER) is an important real world problem that has attracted significant research interest over the past few years. It deals with determining which object descriptions co-refer in a dataset. Due to its practical significance for data mining and data analysis tasks many different ER approaches has been developed to address the ER challenge. This paper proposes a new ER Ensemble framework. The task of ER Ensemble is to combine the results of multiple base-level ER systems into a single solution with the goal of increasing the quality of ER. The framework proposed in this paper leverages the observation that often no single ER method always performs the best, consistently outperforming other ER techniques in terms of quality. Instead, different ER solutions perform better in different contexts. The framework employs two novel combining approaches, which are based on supervised learning. The two approaches learn a mapping of the clustering decisions of the base-level ER systems, together with the local context, into a combined clustering decision. The paper empirically studies the framework by applying it to different domains. The experiments demonstrate that the proposed framework achieves significantly higher disambiguation quality compared to the current state of the art solutions.

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“…Creating multiple partitions of a given data set has been examined in several ways [1,2,5,6,7,8,12,14]. In [1] hyperedges are formed from each clustering solution represented as a label vector.…”

Section: Related Workmentioning

confidence: 99%

“…There has been research on combining ensembles of clustering solutions [1,2,5,6,7,8,12,14] where each clustering solution is represented by a label vector whose Prodip length is equal to the number of examples from which the solution was obtained. It has been shown that combining this ensemble of clustering solutions produces a good final global partition.…”

Section: Introductionmentioning

confidence: 99%

A Cluster Ensemble Framework for Large Data sets

Hore

Hall

Goldgof

2006

2006 IEEE International Conference on Systems, Man and Cybernetics

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Abstract-Combining multiple clustering solutions is important for obtaining a robust clustering solution, merging distributed clustering solutions, and scaling to large data sets. Combining multiple clustering solutions within a scalable and robust framework for large data sets is addressed here. Merging multiple clustering solutions in a scalable framework requires both cluster ensemble creation and merging to be efficient in terms of time and memory complexity. We also introduce the concept of filtering malformed clusters from the ensemble. They result from unfortunate initialization or unbalanced data distribution or noise. Experimental results on real data sets show that this approach will scale and provide cluster partitions which are functionally better or equivalent when compared to clustering all the data at once and clustering solutions contained in the ensemble. We have also compared our algorithm with other relevant ensemble merging and scalable algorithms to point out its strengths and limitations.

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Combining Multiple Clusterings by Soft Correspondence

Abstract: Combining multiple clusterings arises in various impor

Cited by 27 publications

References 11 publications

Integrating community matching and outlier detection for mining evolutionary community outliers

Integrating community matching and outlier detection for mining evolutionary community outliers

Exploiting context analysis for combining multiple entity resolution systems

A Cluster Ensemble Framework for Large Data sets

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