A Survey of Clustering Ensemble Algorithms

Vega-Pons, Sandro; Ruiz-Shulcloper, José

doi:10.1142/s0218001411008683

Cited by 518 publications

(322 citation statements)

References 47 publications

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“…Contributions in this field include the EAC [5] and voting-based algorithms. A comprehensive survey of existing clustering ensemble algorithms is presented in [3]. The votingbased literature utilizes different heuristics in attempting to solve the labelling correspondence problem.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, a given clustering algorithm may give rise to different results for the same data when the initialisation parameters change. Consensus Clustering (CC) [3] methods have addressed this issue by combining solutions obtained from different clustering algorithms into a single consensus solution. In unsupervised learning, this enables more accurate and robust estimation of clusterings when compared to single clustering algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering

Fouad

Randell

Galton

et al. 2017

Communications in Computer and Information Science

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Abstract. We present a framework for adapting consensus clustering methods with superpixels to segment oropharyngeal cancer images into tissue types (epithelium, stroma and background). The simple linear iterative clustering algorithm is initially used to split-up the image into binary superpixels which are then used as clustering elements. Colour features of the superpixels are extracted and fed into several base clustering approaches with various parameter initializations. Two consensus clustering formulations are then used, the Evidence Accumulation Clustering (EAC) and the voting-based function. They both combine the base clustering outcomes to obtain a single more robust consensus result. Unlike most unsupervised tissue image segmentation approaches that depend on individual clustering methods, the proposed approach allows for a robust detection of tissue compartments. For the voting-based consensus function, we introduce a technique based on image processing to generate a consistent labelling scheme among the base clustering outcomes. Experiments conducted on forty five hand-annotated images of oropharyngeal cancer tissue microarray cores show that the ensemble algorithm generates more accurate and stable results than individual clustering algorithms. The clustering performance of the voting-based consensus function using our re-labelling technique also outperforms the existing EAC.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering

Fouad

Randell

Galton

et al. 2017

Communications in Computer and Information Science

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“…These approaches can be classified into two categories [40]: median partition based approaches and object co-occurrence based approaches. In the median partition based approaches, ensemble clustering is cast into an optimization problem that finds the best partition by maximizing the within-cluster similarity, using similarity measures, such as Jaccard coefficient [1], utility function [37] and normalized mutual information [34].…”

Section: Related Workmentioning

confidence: 99%

“…Many algorithms have been proposed for ensemble clustering ( [40] and references therein). Among them, one popular group of approaches is based on the similarity (or co-association) matrix.…”

Section: Introductionmentioning

confidence: 99%

Robust Ensemble Clustering by Matrix Completion

Yang

Jin

et al. 2012

2012 IEEE 12th International Conference on Data Mining

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Abstract-Data clustering is an important task and has found applications in numerous real-world problems. Since no single clustering algorithm is able to identify all different types of cluster shapes and structures, ensemble clustering was proposed to combine different partitions of the same data generated by multiple clustering algorithms. The key idea of most ensemble clustering algorithms is to find a partition that is consistent with most of the available partitions of the input data. One problem with these algorithms is their inability to handle uncertain data pairs, i.e. data pairs for which about half of the partitions put them into the same cluster and the other half do the opposite. When the number of uncertain data pairs is large, they can mislead the ensemble clustering algorithm in generating the final partition. To overcome this limitation, we propose an ensemble clustering approach based on the technique of matrix completion. The proposed algorithm constructs a partially observed similarity matrix based on the data pairs whose cluster memberships are agreed upon by most of the clustering algorithms in the ensemble. It then deploys the matrix completion algorithm to complete the similarity matrix. The final data partition is computed by applying an efficient spectral clustering algorithm to the completed matrix. Our empirical studies with multiple real-world datasets show that the proposed algorithm performs significantly better than the state-of-the-art algorithms for ensemble clustering.

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“…Cluster ensemble methods consist of two stages: generating clusters and calibrating the results to arrive at a consensus [4]. The calibration greatly stabilizes the process and hence addresses the challenge of consistency in results.…”

Section: Introduction "Facts Come From Negotiation Between Differementioning

confidence: 99%

Cluster Ensembles, Majority Vote, Voter Eligibility and Privileged Voters

Charkhabi¹,

Dhot²,

Mojarad³

2014

IJMLC

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Abstract-Grouping items facilitates ideation. AlthoughCluster Analysis has become a classical technique for grouping in science and engineering, to the best of our knowledge it's use remains limited in business. In this application paper we use cluster ensembles to address three barriers to wide scale adoption in the banking industry. The aforementioned challenges are: consistency of results, knowledge beyond the data and grouping with multiple objectives. Contributions of this study include guidance on dealing with the lack of meaningful cluster labels (in the case of ensembles), bimodal cluster distributions and incorporating expert intuition into the clustering process. This application has delivered unobvious insight into a high-dimensional dataset to audiences with diverse backgrounds.

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A Survey of Clustering Ensemble Algorithms

Cited by 518 publications

References 47 publications

Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering

Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering

Robust Ensemble Clustering by Matrix Completion

Cluster Ensembles, Majority Vote, Voter Eligibility and Privileged Voters

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