Universiti Teknologi Malaysia Institutional Repository

Systematic mapping study on granular computing

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

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1016/j.knosys.2015.02.018

Abstract

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.

Item Type:Article
Uncontrolled Keywords:gross alpha and gross beta, radioactivity
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:58878
Deposited By: Haliza Zainal
Deposited On:04 Dec 2016 04:07
Last Modified:07 Apr 2022 02:37

Repository Staff Only: item control page