Cluster Validity Analysis on Soft Set Based Clustering

Authors

  • Rabiei Mamat School of Informatics & Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu Malaysia.
  • Mustafa Mat Deris Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia.
  • Ahmad Shukri Mohd Noor School of Informatics & Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu Malaysia.
  • Sumazly Sulaiman School of Informatics & Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu Malaysia.

Keywords:

Attribute Relative, Categorical Data, Data Clustering, Soft-Set,

Abstract

The issue of data uncertainties are very important in categorical data clustering since the boundary between created clusters are very arguable. Therefore the algorithm called Maximum Attribute Relative (MAR) that is based on the attribute relative of soft-set theory was proposed previously. MAR exploiting the data uncertainties in multi-value information system by introducing a series of clustering attribute. The clusters will be form by using this selected clustering attributes. However, clustering algorithm define clusters that are not-known a priori. Hence, the final clusters of data requires some validation techniques. In this paper, the validity of the clusters produced by MAR was evaluated. The two datasets obtained from UCI-ML repository and an examination results obtained from Malaysian Ministry of Education. The results shows that the clusters produced by MAR has objects similarity up to 99%.

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Published

2017-10-20

How to Cite

Mamat, R., Mat Deris, M., Mohd Noor, A. S., & Sulaiman, S. (2017). Cluster Validity Analysis on Soft Set Based Clustering. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-5), 171–177. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2988

Issue

Vol. 9 No. 3-5: Special Issue on Software Engineering III

Section

Articles

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