Soft set approach for decision attribute selection in data clustering

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...

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Main Author:	Lok, Leh Leong
Format:	Undergraduates Project Papers
Language:	English
Published:	2013
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/8719/ http://umpir.ump.edu.my/id/eprint/8719/ http://umpir.ump.edu.my/id/eprint/8719/1/CD8312%20%40%2073.pdf

id	ump-8719
recordtype	eprints
spelling	ump-87192015-10-28T01:31:07Z http://umpir.ump.edu.my/id/eprint/8719/ Soft set approach for decision attribute selection in data clustering Lok, Leh Leong QA76 Computer software 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 2013 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/8719/1/CD8312%20%40%2073.pdf Lok, Leh Leong (2013) Soft set approach for decision attribute selection in data clustering. Faculty of Computer System And Software Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:81662&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Lok, Leh Leong Soft set approach for decision attribute selection in data clustering
description	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
format	Undergraduates Project Papers
author	Lok, Leh Leong
author_facet	Lok, Leh Leong
author_sort	Lok, Leh Leong
title	Soft set approach for decision attribute selection in data clustering
title_short	Soft set approach for decision attribute selection in data clustering
title_full	Soft set approach for decision attribute selection in data clustering
title_fullStr	Soft set approach for decision attribute selection in data clustering
title_full_unstemmed	Soft set approach for decision attribute selection in data clustering
title_sort	soft set approach for decision attribute selection in data clustering
publishDate	2013
url	http://umpir.ump.edu.my/id/eprint/8719/ http://umpir.ump.edu.my/id/eprint/8719/ http://umpir.ump.edu.my/id/eprint/8719/1/CD8312%20%40%2073.pdf
first_indexed	2023-09-18T22:06:35Z
last_indexed	2023-09-18T22:06:35Z
_version_	1777414748895182848

Soft set approach for decision attribute selection in data clustering

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