Evolutionary multi-objective clustering for overlapping clusters detection

Ripon, Kazi Shah Nawaz; Siddique, M. N. H.

doi:10.1109/cec.2009.4983051

Cited by 26 publications

(18 citation statements)

References 13 publications

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“…Larger crowding distance indicates that a solution is situated in a less-crowded region [Deb et al 2002]. Many other researchers have used this selection strategy for their multiobjective clustering algorithms [Özyer et al 2004;Chen and Wang 2005;Ripon et al 2006aRipon et al , 2006bRipon and Siddique 2009;Won et al 2008;Kim et al 2010;Folino and Pizzuti 2010;Özyer et al 2011;Kirkland et al 2011]. …”

Section: Selection Operatormentioning

confidence: 98%

“…It may be noted that a valid chromosome cannot contain a point index more than once [Mukhopadhyay and Maulik 2007]. A modified medoid-based encoding has been adopted in Ripon and Siddique [2009]. Here, instead of integer encoding, a binary encoding is used.…”

Section: Medoid-basedmentioning

confidence: 99%

“…In Mukhopadhyay and Maulik [2007], a multiobjective clustering algorithm for categorical data is proposed that employs overall deviation Dev(C) (with respect to medoids rather than centroids) and silhouette index as the two objective functions. In Ripon et al [2006a] and Ripon and Siddique [2009], the intracluster entropy H and cluster separation Sep(C) have been optimized simultaneously. In Demir et al [2007], Demir et al [2010], and Kim et al [2010], the Min-Max Cut and the silhouette index have been optimized as the two competing objectives.…”

Section: Use Of Validity Indices As Objective Functionsmentioning

confidence: 99%

“…Note that, after this operation, the donor chromosome decreases in length, whereas the other chromosome becomes a greater length. A similar crossover operation has been adopted in Ripon and Siddique [2009], in which fixed-length binary chromosomes are used to encode the cluster medoids.…”

Section: Crossover Operationmentioning

confidence: 99%

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A Survey of Multiobjective Evolutionary Clustering

2015

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Data clustering is a popular unsupervised data mining tool that is used for partitioning a given dataset into homogeneous groups based on some similarity/dissimilarity metric. Traditional clustering algorithms often make prior assumptions about the cluster structure and adopt a corresponding suitable objective function that is optimized either through classical techniques or metaheuristic approaches. These algorithms are known to perform poorly when the cluster assumptions do not hold in the data. Multiobjective clustering, in which multiple objective functions are simultaneously optimized, has emerged as an attractive and robust alternative in such situations. In particular, application of multiobjective evolutionary algorithms for clustering has become popular in the past decade because of their population-based nature. Here, we provide a comprehensive and critical survey of the multitude of multiobjective evolutionary clustering techniques existing in the literature. The techniques are classified according to the encoding strategies adopted, objective functions, evolutionary operators, strategy for maintaining nondominated solutions, and the method of selection of the final solution. The pros and cons of the different approaches are mentioned. Finally, we have discussed some real-life applications of multiobjective clustering in the domains of image segmentation, bioinformatics, web mining, and so forth.

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Section: Selection Operatormentioning

confidence: 98%

Section: Medoid-basedmentioning

confidence: 99%

Section: Use Of Validity Indices As Objective Functionsmentioning

confidence: 99%

Section: Crossover Operationmentioning

confidence: 99%

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A Survey of Multiobjective Evolutionary Clustering

2015

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“…From the DM perspective, MOEAs are popular underlying optimisation solutions for a variety of DM tasks, enumerated as clustering (Kirkland et al, 2011;Ripon and Siddique, 2009), association rule mining (Matthews et al, 2011;Martín et al, 2011), classification Tan et al, 2014;Pangilinan and Janssens, 2011;Pourpanah et al, 2017), and feature selection (Tan et al, 2014;Venkatadri and Rao, 2010;Brester et al, 2014). For a complete review of the various MOEAs for DM, interested reader is referred to Mukhopadhyay et al (2014a, b).…”

Section: 1mentioning

confidence: 99%

A multi-objective evolutionary algorithm-based soft computing model for educational data mining

Tan

Lim

Bong

et al. 2017

AAOUJ

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For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.*Related content and download information correct at time of download. Purpose -The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students' online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement. Design/methodology/approach -A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide.Findings -The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy. Originality/value -A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed.

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A Multi-objective Evolutionary Algorithm for Color Image Segmentation

Ripon

Ali

Newaz

et al. 2017

Mining Intelligence and Knowledge Exploration

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In this paper, we present a multi-objective segmentation approach for color images. Three objectives, overall deviation, edge value, and connectivity measure, are optimized simultaneously using a multiobjective evolutionary algorithm (MOEA). To demonstrate the effectiveness of the proposed approach, experiments are conducted on benchmark images. The results justify that the proposed approach is able to partition color images in a number of segments consistent with human visual perception. For quantitative evaluation, we extend the existing Probabilistic Rand Index (PRI) considering multi-objective segmentation. The outcomes show that the proposed approach can obtain non-dominated and near-optimal segment solutions satisfying several criteria simultaneously. It can also find the correct number of segments automatically.

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Evolutionary multi-objective clustering for overlapping clusters detection

Cited by 26 publications

References 13 publications

A Survey of Multiobjective Evolutionary Clustering

A Survey of Multiobjective Evolutionary Clustering

A multi-objective evolutionary algorithm-based soft computing model for educational data mining

A Multi-objective Evolutionary Algorithm for Color Image Segmentation

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