Partitions selection strategy for set of clustering solutions

Faceli, Katti; Sakata, Tiemi C.; Souto, Michael Vandesteen Silva; Carvalho, André C. P. L. F. de

doi:10.1016/j.neucom.2010.03.028

Cited by 13 publications

(3 citation statements)

References 22 publications

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“…As this paper focuses on a data-driven investigation of MOCLE, MOCK, and ∆-MOCK, it naturally shares similarities with other works [2,[8][9][10]12]. Differently from these works, in this paper, besides performing a quantitative analysis identifying which methods present a good/poor performance concerning another, we will also conduct a more detailed/qualitative analysis to provide some insights on why such a behavior happened.…”

Section: Related Work and Backgroundmentioning

confidence: 94%

“…Finally, it is worth noting that the core ideas of MOCLE, as well as those of MOCK and ∆-MOCK, are not linked to specific objective functions, crossover operator, and search algorithm. For instance, in terms of objective functions, like MOCK, MOCLE has been mainly implemented using dev and con [2,[8][9][10]. Concerning the crossover operators, the software available at http://lasid.sor.…”

Section: Mocle (Multi-objective Clustering Ensemblementioning

confidence: 99%

See 1 more Smart Citation

Multi-objective Clustering: A Data-driven Analysis of MOCLE, MOCK and $Δ$-MOCK

Kultzak¹,

Morimoto²,

Pozo³

et al. 2021

Preprint

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We present a data-driven analysis of MOCK, ∆-MOCK, and MOCLE. These are three closely related approaches that use multiobjective optimization for crisp clustering. More specifically, based on a collection of 12 datasets presenting different proprieties, we investigate the performance of MOCLE and MOCK compared to the recently proposed ∆-MOCK. Besides performing a quantitative analysis identifying which method presents a good/poor performance with respect to another, we also conduct a more detailed analysis on why such a behavior happened. Indeed, the results of our analysis provide useful insights on the strengths and weaknesses of the methods investigated.

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Section: Related Work and Backgroundmentioning

confidence: 94%

Section: Mocle (Multi-objective Clustering Ensemblementioning

confidence: 99%

Multi-objective Clustering: A Data-driven Analysis of MOCLE, MOCK and $Δ$-MOCK

Kultzak¹,

Morimoto²,

Pozo³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Wei (2005) used a bagging algorithm for the purpose of selecting solutions and adopted a spectral clustering algorithm as the consensus function (Zhang and Cao 2014). In addition, Dai et al (2015), Faceli et al (2010) and Yang et al (2017) presented other SCE algorithms.…”

Section: Proof the Average Quality Ofmentioning

confidence: 99%

Multiple clustering and selecting algorithms with combining strategy for selective clustering ensemble

et al. 2020

Soft Comput

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Clustering ensemble can overcome the instability of clustering and improve clustering performance. With the rapid development of clustering ensemble, we find that not all clustering solutions are effective in their final result. In this paper, we focus on selection strategy in selective clustering ensemble. We propose a multiple clustering and selecting approach (MCAS), which is based on different original clustering solutions. Furthermore, we present two combining strategies, direct combining and clustering combining, to combine the solutions selected by MCAS. These combining strategies combine results of MCAS and get a more refined subset of solutions, compared with traditional selective clustering ensemble algorithms and single clustering and selecting algorithms. Experimental results on UCI machine learning datasets show that the algorithm that uses multiple clustering and selecting algorithms with combining strategy performs well on most datasets and outperforms most selective clustering ensemble algorithms. KeywordsSelective clustering ensemble • Clustering solution • Multiple clustering and selecting algorithms • Combining strategy IntroductionClustering is one of the most important tools in data mining. The major goal of clustering is to seek a grouping Communicated by A. Di Nola.

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