Repository Universitas Ahmad Dahlan

A Modified Fuzzy k-Partition Based on Indiscernibility Relation for Categorical Data Clustering

Yanto, Iwan Tri Riyadi and Akmar Ismail, Maizatul and Herawan, Tutut (2018) A Modified Fuzzy k-Partition Based on Indiscernibility Relation for Categorical Data Clustering. [Artikel Dosen]

[img]
Preview
Text
A Modified Fuzzy k-Partition Based on Indiscernibility Relation for Categorical Data Clustering.pdf

Download (515kB) | Preview

Abstract

Categorical data clustering has been adopted by many scientific communities to classify objects from large databases. In order to classify the objects, Fuzzy k-Partition approach has been proposed for categorical data clustering. However, existing Fuzzy k-Partition approaches suffer from high computa-tional time and low clustering accuracy. Moreover, the parameter maximize of the classification like-lihood function in Fuzzy k-Partition approach will always have the same categories, hence producing the same results. To overcome these issues, we propose a modified Fuzzy k-Partition based on indiscern-ibility relation. The indiscernibility relation induces an approximation space which is constructed by equivalence classes of indiscernible objects, thus it can be applied to classify categorical data. The novelty of the proposed approach is that unlike previous approach that use the likelihood function of multi-variate multinomial distributions, the proposed approach is based on indescernibility relation. We per-formed an extensive theoretical analysis of the proposed approach to show its effectiveness in achieving lower computational complexity. Further, we compared the proposed approach with Fuzzy Centroid and Fuzzy k-Partition approaches in terms of response time and clustering accuracy on several UCI bench-mark and real world datasets. The results show that the proposed approach achieves lower response time and higher clustering accuracy as compared to other Fuzzy k-based approaches.

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/11614

Actions (login required)

View Item View Item

Repository Universitas Ahmad Dahlan is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.