Cluster validation analysis on attribute relative of soft-set theory

Mamat, Rabiei and Mohd Noor, Ahmad Shukri and Herawan, Tutut and Mat Deris, Mustafa (2017) Cluster validation analysis on attribute relative of soft-set theory. In: Advances in Intelligent Systems and Computing. Recent Advances on Soft Computing and Data Mining, 549 . Springer International Publishing, pp. 3-10. ISBN 9783319512792

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Official URL: DOI:10.1007/978-3-319-51281-5_1


Data clustering on categorical data pose a difficult challenge since there are no-inherent distance measures between data values. One of the approaches that can be used is by introducing a series of clustering attributes in the categorical data. By this approach, Maximum Total Attribute Relative (MTAR) technique that is based on the attribute relative of soft-set theory has been proposed and proved has better execution time as compared to other equivalent techniques that used the same approach. In this paper, the cluster validity analysis on the technique is explained and discussed. In this analysis, the validity of the clusters produced by MTAR technique is evaluated by the entropy measure using two standards dataset: Soybean (Small) and Zoo from University California at Irvine (UCI) repository. Results show that the clusters produce by MTAR technique have better entropy and improved the clusters validity up to 33%.

Item Type:Book Section
Uncontrolled Keywords:Soft set; data clustering; attribute relative; cluster validity
Subjects:Q Science > QA Mathematics > QA76 Computer software
Divisions:Faculty of Computer Science and Information Technology > Department of Software Engineering
ID Code:9598
Deposited By:Mr. Mohammad Shaifulrip Ithnin
Deposited On:13 Aug 2018 11:34
Last Modified:13 Aug 2018 11:34

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