Hybrid strategy for selecting compact set of clustering partitions

Antunes, Vanessa; Sakata, Tiemi C.; Faceli, Katti; Souto, Michael Vandesteen Silva

doi:10.1016/j.asoc.2019.105971

Cited by 4 publications

(5 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: 95%

“…The con is computed according to (2), where n is the number of objects in the dataset, L is the parameter that determines the number of nearest neighbors that contributes to the connectivity, a ij is the jth nearest neighbor of object x i , and c k is a cluster that belongs to a partition π. Depending on the value chosen for parameter L, different partitions could present the same optimal value for con [12].…”

Section: Mockmentioning

confidence: 99%

“…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%

“…An important issue not tackled in this paper concerns, after generating the set of final solutions (partitions), the model selection phase. The work in [2,25], for instance, presents promising approaches to address this question.…”

Section: Final Remarksmentioning

confidence: 99%

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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: 95%

Section: Mockmentioning

confidence: 99%

Section: Mocle (Multi-objective Clustering Ensemblementioning

confidence: 99%

Section: Final Remarksmentioning

confidence: 99%

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

Kultzak¹,

Morimoto²,

Pozo³

et al. 2021

Preprint

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“…Frameworks like multiobjective clustering with automatic k-determination (MOCK) [ 2 ] and multi-objective clustering ensemble algorithm (MOCLE) [ 3 ] implement multi-objective evolutionary algorithms in their solutions. [ 4 ] proposed a hybrid algorithm, called Hybrid Selection Strategy (HSS), for selection of clustering partitions, combining multi-objective clustering and partition selection techniques.…”

Section: Introductionmentioning

confidence: 99%

Improving the Clustering Algorithms Automatic Generation Process with Cluster Quality Indexes

Montenegro

Meiguins

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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AutoClustering is a computational tool for the automatic generation of clustering algorithms, which combines and evaluates the main parts of density-based algorithms to generate more appropriate solutions for a given dataset for clustering tasks. AutoClustering uses the Estimation of Distribution Algorithms (EDA) evolutionary technique to create the algorithms (individuals), and the adapted CLEST method (originally determines the best number of groups for a dataset) to compute individual fitness, using a decision-tree classifier. Thus, as the motivation to improve the quality of the results generated by AutoClustering, and to avoid possible bias by the adoption of a classifier, this work proposes to increase the efficiency of the evaluation process by the addition of a quality metric based on a fusion of three quality indexes of solution clusters. The three quality indexes are Silhouette, Dunn, and Davies-Bouldin, which assess the situation Intra and Inter clusters, with algorithms based on distance and independent of the generation of the groups. A final score for a specific solution (algorithm + parameters) is the average of normalized quality metric and normalized fitness. Besides, the results of the proposal presented solutions with higher cluster quality metrics, higher fitness average, and higher diversity of generated individuals (clustering algorithms) when compared with traditional Autocluestering.

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