Evolutionary multi-objective automatic clustering enhanced with quality metrics and ensemble strategy

Zhu, Shuwei; Xu, Lihong; Goodman, Erik D.

doi:10.1016/j.knosys.2019.105018

Cited by 36 publications

(18 citation statements)

References 52 publications

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“…Since the aims of clustering are to maximize the similarity within the same cluster and dissimilarity across clusters, it can be considered an optimization problem [26]. In optimization problems, it is often necessary to maximize or minimize some objective function (the function used to evaluate the quality of the solution).…”

Section: Literature Reviewmentioning

confidence: 99%

Gradient-based elephant herding optimization for cluster analysis

Duan

Liu

et al. 2022

Appl Intell

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Clustering analysis is essential for obtaining valuable information from a predetermined dataset. However, traditional clustering methods suffer from falling into local optima and an overdependence on the quality of the initial solution. Given these defects, a novel clustering method called gradient-based elephant herding optimization for cluster analysis (GBEHO) is proposed. A well-defined set of heuristics is introduced to select the initial centroids instead of selecting random initial points. Specifically, the elephant optimization algorithm (EHO) is combined with the gradient-based algorithm GBO for assigning initial cluster centers across the search space. Second, to overcome the imbalance between the original EHO exploration and exploitation, the initialized population is improved by introducing Gaussian chaos mapping. In addition, two operators, i.e., random wandering and variation operators, are set to adjust the location update strategy of the agents. Nine datasets from synthetic and real-world datasets are adopted to evaluate the effectiveness of the proposed algorithm and the other metaheuristic algorithms. The results show that the proposed algorithm ranks first among the 10 algorithms. It is also extensively compared with state-of-the-art techniques, and four evaluation criteria of accuracy rate, specificity, detection rate, and F-measure are used. The obtained results clearly indicate the excellent performance of GBEHO, while the stability is also more prominent.

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Section: Literature Reviewmentioning

confidence: 99%

Gradient-based elephant herding optimization for cluster analysis

Duan

Liu

et al. 2022

Appl Intell

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“…However, this strategy is very popular because of its simplicity and effectiveness in testing the algorithms against hard evaluation scenarios. On the other hand, the other approaches [14,13,8,15,24,36,10,38,21,39] consider high-quality partitions in the initialization generated by clustering algorithms. In this article, we evaluate this second strategy, considering the initialization of established EMOCs: MO-CLE (Multi-Objective Clustering Ensemble) [8] and MOCK (Multi-Objective…”

Section: Evolutionary Multi-objective Clustering Approachesmentioning

confidence: 99%

“…MOCK-medoid [14] uses only the KM in the initialization. Most recent works derived from MOCK, such as those in [10,38,39], rely solely on MST-clustering to generate the initial population.…”

Section: Evolutionary Multi-objective Clustering Approachesmentioning

confidence: 99%

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An Analysis of the Admissibility of the Objective Functions Applied in Evolutionary Multi-objective Clustering

Morimoto¹,

Pozo²,

Souto³

2022

Preprint

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A variety of clustering criteria has been applied as an objective function in Evolutionary Multi-Objective Clustering approaches (EMOCs). However, most EMOCs do not provide detailed analysis regarding the choice and usage of the objective functions. Aiming to support a better choice and definition of the objectives in the EMOCs, this paper proposes an analysis of the admissibility of the clustering criteria in evolutionary optimization by examining the search direction and its potential in finding optimal results. As a result, we demonstrate how the admissibility of the objective functions can influence the optimization. Furthermore, we provide insights regarding the combinations and usage of the clustering criteria in the EMOCs.

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“…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%

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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Evolutionary multi-objective automatic clustering enhanced with quality metrics and ensemble strategy

Cited by 36 publications

References 52 publications

Gradient-based elephant herding optimization for cluster analysis

Gradient-based elephant herding optimization for cluster analysis

An Analysis of the Admissibility of the Objective Functions Applied in Evolutionary Multi-objective Clustering

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

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