Robust Ensemble Clustering by Matrix Completion

Yi, Jinfeng; Yang, Tianbao; Jin, Rong; Jain, Anil K.; Mahdavi, Mehrdad

doi:10.1109/icdm.2012.123

Cited by 67 publications

(65 citation statements)

References 31 publications

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“…Another strategy is to use a different clustering algorithm for each member [12,45] with a hope that different algorithms may generate more diverse members. Yi et al [45] used some well-known clustering algorithms, such as hierarchical clustering and k-means.…”

Section: Ensemble Member Generationmentioning

confidence: 99%

See 1 more Smart Citation

Clustering ensemble method

Alqurashi

Wang

2018

Int. J. Mach. Learn. & Cyber.

103

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A clustering ensemble aims to combine multiple clustering models to produce a better result than that of the individual clustering algorithms in terms of consistency and quality. In this paper, we propose a clustering ensemble algorithm with a novel consensus function named Adaptive Clustering Ensemble. It employs two similarity measures, cluster similarity and a newly defined membership similarity, and works adaptively through three stages. The first stage is to transform the initial clusters into a binary representation, and the second is to aggregate the initial clusters that are most similar based on the cluster similarity measure between clusters. This iterates itself adaptively until the intended candidate clusters are produced. The third stage is to further refine the clusters by dealing with uncertain objects to produce an improved final clustering result with the desired number of clusters. Our proposed method is tested on various real-world benchmark datasets and its performance is compared with other state-of-the-art clustering ensemble methods, including the Co-association method and the Meta-Clustering Algorithm. The experimental results indicate that on average our method is more accurate and more efficient.

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Section: Ensemble Member Generationmentioning

confidence: 99%

“…Yi et al [45] used some well-known clustering algorithms, such as hierarchical clustering and k-means. Gionis et al [12] used the single, average, ward and complete linkage methods and k-means to generate ensemble members.…”

Section: Ensemble Member Generationmentioning

confidence: 99%

Clustering ensemble method

Alqurashi

Wang

2018

Int. J. Mach. Learn. & Cyber.

103

View full text Add to dashboard Cite

show abstract

“…We also performed a paired -test to assess the statistical significance of the results; the significance level was always set to 0.05. We compare the proposed WOEC methods against eight state-of-the-art ensemble clustering algorithms, namely: CSPA [2], MCLA [2], HBGF [16], BCE [19], WCC [21], ECMC [22], WSPA [3], [4] and WBPA [3], [4]. (HGPA [2] was not included in our experiments since it typically performs worse than CSPA and MCLA.…”

Section: Results and Analysismentioning

confidence: 99%

“…More recently, a method called ensemble clustering by matrix completion (ECMC) was proposed [22]. ECMC uses the reliable pair of objects to construct a partially observed co-association matrix, and exploits the matrix completion algorithm to replenish the missing entries of the co-association matrix.…”

Section: Introductionmentioning

confidence: 99%

Weighted-Object Ensemble Clustering

Ren

Domeniconi

Zhang

et al. 2013

2013 IEEE 13th International Conference on Data Mining

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Abstract-Ensemble clustering, also known as consensus clustering, aims to generate a stable and robust clustering through the consolidation of multiple base clusterings. In recent years many ensemble clustering methods have been proposed, most of which treat each clustering and each object as equally important. Some approaches make use of weights associated with clusters, or with clusterings, when assembling the different base clusterings. Boosting algorithms developed for classification have also led to the idea of considering weighted objects during the clustering process. However, not much effort has been put towards incorporating weighted objects into the consensus process.To fill this gap, in this paper we propose an approach called Weighted-Object Ensemble Clustering (WOEC). We first estimate how difficult it is to cluster an object by constructing the coassociation matrix that summarizes the base clustering results, and we then embed the corresponding information as weights associated to objects. We propose three different consensus techniques to leverage the weighted objects. All three reduce the ensemble clustering problem to a graph partitioning one. We present extensive experimental results which demonstrate that our WOEC approach outperforms state-of-the-art consensus clustering methods and is robust to parameter settings.

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“…Ensemble clustering is an active machine learning research area (Yi et al 2012). However, the literature shows that few research efforts have used ensemble clustering algorithms in scientometrics to analyze scientific structure.…”

Section: Ensemble Clusteringmentioning

confidence: 99%

Multi-source data fusion study in scientometrics

Yue

Wang

et al. 2017

Scientometrics

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This paper provides an introduction to multi-source data fusion (MSDF) and comprehensively overviews the ingredients and challenges of MSDF in scientometrics. As compared to the MSDF methods in the sensor and other fields, and considering the features of scientometrics, in this paper an application model and procedure of MSDF in scientometrics are proposed. The model and procedure can be divided into three parts: data type integration, fusion of data relations, and ensemble clustering. Furthermore, the fusion of data relations can be divided into cross-integration of multi-mode data and matrix fusion of multi-relational data. To obtain a clearer and deeper analysis of the MSDF model, this paper further focuses on the application of MSDF in topic identification based on text analysis of scientific literatures. This paper also discusses the application of MSDF for the exploration of scientific literatures. Finally, the most suitable MSDF methods for different situations are discussed.

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Robust Ensemble Clustering by Matrix Completion

Cited by 67 publications

References 31 publications

Clustering ensemble method

Clustering ensemble method

Weighted-Object Ensemble Clustering

Multi-source data fusion study in scientometrics

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