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A Framework of Fuzzy Partition Based on Artificial Bee Colony for Categorical Data Clustering

Tri Riyadi Yanto, Iwan and Saadi, Younes and Hartama, Dedy and Pramudi Ismi, Dewi and Pranolo, Andri (2018) A Framework of Fuzzy Partition Based on Artificial Bee Colony for Categorical Data Clustering. [Artikel Dosen]

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Abstract

Abstract - Fuzzy k-partition (FkP) is an effective clustering technique, which il mathematical model based. Thus, the objective function of FkP is a nonlinear function. Membership random selection is featured by an iterative process, which results in local optima traps easily. It is important to find global optimal consider to nonlinear objective function of the problem. Moreover, Artificial Bee colony (ABC) has ability and efficiently used for multivariable, multinomial function optimization. To this, this paper proposes the hybridization of FkP based on Artilicial Bee colony (ABC) a pnpulation based algorltbm. Some of benchmarks data sets have been elaborated to test the proposed approach. The experiment shows that FkP ABC obtains better results in term of the dun index validity clustering as compared to the baseline algorithm. Keywords - data clustering; fuzzy k-partition; artifical bee colony

Item Type: Artikel Dosen
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisi / Prodi: Faculty of Mathematics and Natural Sciences (Fakultas Matematika dan Ilmu Pengetahuan Alam) > S1-Information System (S1-Sistem Informasi)
Depositing User: Iwan Tri Riyadi Yanto
Date Deposited: 06 Nov 2018 22:17
Last Modified: 06 Nov 2018 22:17
URI: http://eprints.uad.ac.id/id/eprint/11612

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