Systematic mapping study on granular computing
Granular computing has attracted many researchers as a new and rapidly growing paradigm of information processing. In this paper, we apply systematic mapping study to classify the granular computing researches to discover relative derivations to specify its research strength and quality. Our search...
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my.utm.588782022-04-07T02:37:15Z http://eprints.utm.my/id/eprint/58878/ Systematic mapping study on granular computing Salehi, Saber Selamat, Ali Fujita, Hamido QA75 Electronic computers. Computer science Granular computing has attracted many researchers as a new and rapidly growing paradigm of information processing. In this paper, we apply systematic mapping study to classify the granular computing researches to discover relative derivations to specify its research strength and quality. Our search scope is limited to the Science Direct and IEEE Transactions papers published between January 2012 and August 2014. We defined four perspectives of classification schemes to map the selected studies that are focus area, contribution type, research type and framework. Results of mapping the selected studies show that almost half of the research focused area belongs to category of data analysis. In addition, most of the selected papers belong to proposing the solutions in research type scheme. Distribution of papers between tool, method and enhancement categories of contribution type are almost equal. Moreover, 39% of the relevant papers belong to the rough set framework. The results show that there is little attention paid to cluster analysis in existing frameworks to discover granules for classification. We applied five clustering algorithms on three datasets from UCI repository to compare the form of information granules, and then classify the patterns and define them to a specific class based on their geometry and belongings. The clustering algorithms are DBSCAN, c-means, k-means, GAk-means and Fuzzy-GrC and the comparison of information granules are based on the coverage, misclassification and accuracy. The survey of experimental results mostly shows Fuzzy-GrC and GAk-means algorithm superior to other clustering algorithms; while, c-means clustering algorithm shows inferior to other clustering algorithms. Elsevier B.V. 2015 Article PeerReviewed Salehi, Saber and Selamat, Ali and Fujita, Hamido (2015) Systematic mapping study on granular computing. Knowledge-Based Systems, 80 . pp. 78-97. ISSN 0950-7051 http://dx.doi.org/10.1016/j.knosys.2015.02.018 DOI: 10.1016/j.knosys.2015.02.018 |
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QA75 Electronic computers. Computer science Salehi, Saber Selamat, Ali Fujita, Hamido Systematic mapping study on granular computing |
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Granular computing has attracted many researchers as a new and rapidly growing paradigm of information processing. In this paper, we apply systematic mapping study to classify the granular computing researches to discover relative derivations to specify its research strength and quality. Our search scope is limited to the Science Direct and IEEE Transactions papers published between January 2012 and August 2014. We defined four perspectives of classification schemes to map the selected studies that are focus area, contribution type, research type and framework. Results of mapping the selected studies show that almost half of the research focused area belongs to category of data analysis. In addition, most of the selected papers belong to proposing the solutions in research type scheme. Distribution of papers between tool, method and enhancement categories of contribution type are almost equal. Moreover, 39% of the relevant papers belong to the rough set framework. The results show that there is little attention paid to cluster analysis in existing frameworks to discover granules for classification. We applied five clustering algorithms on three datasets from UCI repository to compare the form of information granules, and then classify the patterns and define them to a specific class based on their geometry and belongings. The clustering algorithms are DBSCAN, c-means, k-means, GAk-means and Fuzzy-GrC and the comparison of information granules are based on the coverage, misclassification and accuracy. The survey of experimental results mostly shows Fuzzy-GrC and GAk-means algorithm superior to other clustering algorithms; while, c-means clustering algorithm shows inferior to other clustering algorithms. |
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Salehi, Saber Selamat, Ali Fujita, Hamido |
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Salehi, Saber Selamat, Ali Fujita, Hamido |
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Salehi, Saber |
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Systematic mapping study on granular computing |
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Systematic mapping study on granular computing |
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Systematic mapping study on granular computing |
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Systematic mapping study on granular computing |
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Systematic mapping study on granular computing |
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systematic mapping study on granular computing |
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Elsevier B.V. |
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2015 |
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http://eprints.utm.my/id/eprint/58878/ http://dx.doi.org/10.1016/j.knosys.2015.02.018 |
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