Combining multiple clusterings using similarity graph

Mimaroglu, Selim; Erdil, Ertunç

doi:10.1016/j.patcog.2010.09.008

Cited by 75 publications

(30 citation statements)

References 11 publications

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“…The CA matrix is a classical and widely used tool for dealing with the ensemble clustering problem [20], [24], [28], [49]. Despite the significant success, one limitation of the CA matrix is that it treats all clusters and all base clusterings in the ensemble equally and lack the ability to evaluate and weight the ensemble members w.r.t.…”

Section: Refining Co-association Matrix By Local Weightingmentioning

confidence: 99%

Locally Weighted Ensemble Clustering

Huang¹,

Wang²,

Lai³

2018

IEEE Trans. Cybern.

310

174

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Abstract-Due to its ability to combine multiple base clusterings into a probably better and more robust clustering, the ensemble clustering technique has been attracting increasing attention in recent years. Despite the significant success, one limitation to most of the existing ensemble clustering methods is that they generally treat all base clusterings equally regardless of their reliability, which makes them vulnerable to low-quality base clusterings. Although some efforts have been made to (globally) evaluate and weight the base clusterings, yet these methods tend to view each base clustering as an individual and neglect the local diversity of clusters inside the same base clustering. It remains an open problem how to evaluate the reliability of clusters and exploit the local diversity in the ensemble to enhance the consensus performance, especially in the case when there is no access to data features or specific assumptions on data distribution. To address this, in this paper, we propose a novel ensemble clustering approach based on ensemble-driven cluster uncertainty estimation and local weighting strategy. In particular, the uncertainty of each cluster is estimated by considering the cluster labels in the entire ensemble via an entropic criterion. A novel ensemble-driven cluster validity measure is introduced, and a locally weighted co-association matrix is presented to serve as a summary for the ensemble of diverse clusters. With the local diversity in ensembles exploited, two novel consensus functions are further proposed. Extensive experiments on a variety of real-world datasets demonstrate the superiority of the proposed approach over the state-of-the-art.

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Section: Refining Co-association Matrix By Local Weightingmentioning

confidence: 99%

Locally Weighted Ensemble Clustering

Huang¹,

Wang²,

Lai³

2018

IEEE Trans. Cybern.

310

174

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“…This problem has been studied by many researchers in data mining and many contributions have been developed to¯nd MCDA Clustering Approach Based on Clustering Ensemble 819 consensus clustering such as: hyper graph partitioning, 7 voting approach, [38][39][40][41][42][43][44][45][46][47][48][49] quadratic mutual information algorithm 5 and distance-based methods. 38 Optimization techniques have been also explored to solve the problem of clustering ensemble; Hornik and B€ ohm.…”

Section: Related Work In Clustering Ensemblementioning

confidence: 99%

“…This work has been extended to de¯ne relations between the clusters. [40][41][42][43][44][45][46][47][48][49][50] The problem of de¯ning relations between clusters has been also tackled in Ref. 34 where a formalization of the problem has been proposed using the preference relations.…”

Section: Introductionmentioning

confidence: 99%

A Multicriteria Clustering Approach Based on Similarity Indices and Clustering Ensemble Techniques

Rouba

Bahloul

2014

Int. J. Info. Tech. Dec. Mak.

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This paper deals with the problem of multicriteria clusters construction. The aim is to propose a multicriteria clustering procedure aiming at discovering data structures from a multicriteria perspective by de¯ning a dissimilarity measure which takes into account the multicriteria nature of the problem. Comparing two objects in the multicriteria context is based on the preference information that expresses whether these objects are indi®erent, incomparable or one is preferred to the other. The proposed approach uses this preference information with an agreement-disagreement similarity index to compute a dissimilarity measure. The approach generates, according to the preference relations, a set of clusterings. Each clustering expresses a way of grouping objects according to the preference relation used. A good quality¯nal clustering is obtained by combining the clusterings generated previously using a clustering ensemble technique.

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“…Similarity Within Cluster (SWC), or compactness, is the measure of how similar the objects inside a cluster [2]. Similarity Within Cluster Ci, which is pictured in Fig.4, is calculated as shown in …”

Section: Swc (Similarity Within Cluster)mentioning

confidence: 99%

Clustering fusion with automatic cluster number

Muneeswaran

Velvizhy

Kannan

2014

2014 International Conference on Recent Trends in Information Technology

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Most of the real world applications use data clustering techniques for effective data analysis. All clustering techniques have some assumptions on the underlying dataset. We can get accurate clusters if the assumptions hold good. But it is difficult to satisfy all assumptions. Currently, not a single clustering algorithm is available to find all types of cluster shapes and structures. Therefore, an ensemble clustering algorithm is proposed in this paper in order to produce accurate clusters. Moreover, the existing clustering ensemble methods require more number of clusters in advance to produce final clusters. In this paper, we propose a novel method which groups a set of clusters into accurate final clusters to enhance the decision accuracy. This method does not need the number of clusters as input but produces the clusters automatically assuming the no of clusters. Index Terms-Clustering, Clustering ensemble.I.

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Combining multiple clusterings using similarity graph

Cited by 75 publications

References 11 publications

Locally Weighted Ensemble Clustering

Locally Weighted Ensemble Clustering

A Multicriteria Clustering Approach Based on Similarity Indices and Clustering Ensemble Techniques

Clustering fusion with automatic cluster number

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