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Soft set approach for decision attribute selection in data clustering

Lok, Leh Leong (2013) Soft set approach for decision attribute selection in data clustering. Faculty of Computer System And Software Engineering, Universiti Malaysia Pahang.

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Abstract

Clustering is one of the fundamental operations in data mining that cluster set of heterogeneous data objects into smaller homogeneous classes. Using clustering attribute (decision attribute) is one of the data clustering techniques. Soft set theory is a new mathematical tool applying in clustering applications in databases circumstances. Hence,the research aim is to find the practical technique of soft set theory for decision attribute selection in soft set theory. The test is been done by using two UCI benchmark datasets to determine the speed of execution time for soft set approach with rough set techniques, that are Total Roughness (TR), Min-Min Roughness (MMR) and Maximum Dependency of Attributes (MDA). The results show that the proposed technique provides faster decision for selecting a clustering attribute

Item Type: Undergraduates Project Papers
Additional Information: Project paper (Bachelor of Computer Science (Software Engineering) -- Universiti Malaysia Pahang - 2013
Uncontrolled Keywords: Computer software Development;Application software Development;Data clustering
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Muhamad Firdaus Janih@Jaini
Date Deposited: 28 Oct 2015 01:31
Last Modified: 28 Oct 2015 01:31
URI: http://umpir.ump.edu.my/id/eprint/8719
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