Simultaneous feature selection and clustering for categorical features using multi objective genetic algorithm

Dutta, Dipankar; Dutta, Paramartha; Sil, Jaya

doi:10.1109/his.2012.6421332

Cited by 10 publications

(8 citation statements)

References 22 publications

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“…For example, in [92,93], 𝐻 , 𝑆𝑒𝑝 𝐴 𝐿, 𝐷𝑢𝑛𝑛, and 𝐷𝑒𝑣 are evaluated to analyze the general behavior of the EMOC approaches regarding each criterion. In [22,23], they compare their approaches with other ones based on the 𝐷𝐵, 𝐻 and 𝑆𝑒𝑝 𝐴𝐿 .…”

Section: Multi-objective Evolutionary Clustering Evaluationmentioning

confidence: 99%

A Review of Evolutionary Multi-objective Clustering Approaches

Morimoto¹,

Pozo²,

Souto³

2021

Preprint

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This article presents how the studies of the evolutionary multi-objective clustering have been evolving over the years, based on a mapping of the indexed articles in the ACM, IEEE, and Scopus. We present the most relevant approaches considering the highimpact journals and conferences to provide an overview of this study field. We analyzed the algorithms based on the features and components presented in the proposed general architecture of the evolutionary multi-objective clustering. These algorithms were grouped considering common clustering strategies and applications. Furthermore, issues regarding the difficulty in defining appropriate clustering criteria applied to evolutionary multi-objective clustering and the importance of the evolutionary process evaluation to have a clear view of the optimization efficiency are discussed. It is essential to observe these aspects besides specific clustering properties when designing new approaches or selecting/using the existing ones. Finally, we present other potential subjects of future research, in which this article can contribute to newcomers or busy researchers who want to have a wide vision of the field.

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Section: Multi-objective Evolutionary Clustering Evaluationmentioning

confidence: 99%

A Review of Evolutionary Multi-objective Clustering Approaches

Morimoto¹,

Pozo²,

Souto³

2021

Preprint

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“…[78] The algorithms used for comparison were the SGA [80] and MOGA. [81] The MS of a clustering result C with reference to T , the matrix corresponding to the correct clustering, is defined as:…”

Section: Scenario Imentioning

confidence: 99%

Bio-inspired multiobjective clustering optimization: A survey and a proposal

Cunha

Cruz

Politi

et al. 2017

AIR

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Multiobjective clustering techniques have been used to simultaneously consider several complementary aspects of clustering quality. They optimize two or more cluster validity indices simultaneously, they lead to high-quality results, and have emerged as attractive and robust alternatives for solving clustering problems. This paper provides a brief review of bio-Inspired multiobjective clustering, and proposes a bee-inspired multiobjective optimization (MOO) algorithm, named cOptBees-MO, to solve multiobjective data clustering problems. In its survey part, a brief tutorial on MOO and multiobjective clustering optimization (MOCO) is presented, followed by a review of the main works in the area. Particular attention is given to the many objective functions used in MOCO. To evaluate the performance of the algorithm it was executed for various datasets and the results presented high quality clusters, diverse solutions an the automatic determination of a suitable number of clusters.

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“…However, only a few MOGA clustering approaches have been proposed so far and their experimental results have demonstrated that MOGA clustering approaches significantly outperform existing SGA clustering approaches [26,27]. Earlier work on clustering by MOGA are [1,7,[11][12][13][14][15]17,22,29,46,47,51,56,57,[61][62][63]. In [14,15] we have done simultaneous feature selection and clustering for categorical and continuous features respectively.…”

Section: Previous Workmentioning

confidence: 99%

“…categorical and continuous features both), but simultaneous feature selection was not done. Multi objective clustering on categorical data has been done in [7,12,14,22,51,56,57]. In [1,11,13,29,46,47,[61][62][63] researchers have done clustering on numerical data set.…”

Section: Previous Workmentioning

confidence: 99%

Simultaneous feature selection and clustering with mixed features by multi objective genetic algorithm

Dutta

Sil

2013

HIS

Self Cite

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In this paper, we propose a novel evolutionary clustering algorithm for mixed type data (numerical and categorical). It is doing clustering and feature selection simultaneously. Feature subset selection improves quality of clustering. It also improves understandability and scalability. It unfastens attraction on numerical or categorical dataset only. K-prototype (KP) is a wellknown partitional clustering algorithm for mixed type data. However, this type of algorithm is sensitive to initialization and may converge to local optima. It is optimizing a single measure only i.e. minimizations of intra cluster distance. We have considered clustering as a multi objective optimization problem (MOOP). Minimization of intra cluster distance and maximization of inter cluster distance are two objectives of optimization. Multi objective genetic algorithm (MOGA) is a well-known algorithm which can be applicable for MOOP to find out near global optimal solution. So in this paper we have developed a hybridized genetic clustering algorithm by combining the global search ability of MOGA and local search ability of KP. Experiments on real-life benchmark datasets from UCI machine learning repository prove the superiority of the proposed algorithm.

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Simultaneous feature selection and clustering for categorical features using multi objective genetic algorithm

Cited by 10 publications

References 22 publications

A Review of Evolutionary Multi-objective Clustering Approaches

A Review of Evolutionary Multi-objective Clustering Approaches

Bio-inspired multiobjective clustering optimization: A survey and a proposal

Simultaneous feature selection and clustering with mixed features by multi objective genetic algorithm

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