A Novel Soft Set Approach in Selecting Clustering Attribute

Clustering is one of the most useful tasks in data mining process for discovering groups and identifying interesting distributions and patterns in the underlying data. One of the techniques of data clustering was performed by introducing a clustering attribute. Soft set theory, initiated by Molodtso...

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Main Authors:	Qin, Hongwu, Ma, Xiuqin, Jasni, Mohamad Zain, Herawan, Tutut
Format:	Article
Language:	English
Published:	Elsevier 2012
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/6188/ http://umpir.ump.edu.my/id/eprint/6188/ http://umpir.ump.edu.my/id/eprint/6188/ http://umpir.ump.edu.my/id/eprint/6188/1/fskkp-2012-jasni-novel_soft_set_approach_abs_only.pdf

id	ump-6188
recordtype	eprints
spelling	ump-61882018-05-21T07:43:23Z http://umpir.ump.edu.my/id/eprint/6188/ A Novel Soft Set Approach in Selecting Clustering Attribute Qin, Hongwu Ma, Xiuqin Jasni, Mohamad Zain Herawan, Tutut QA75 Electronic computers. Computer science Clustering is one of the most useful tasks in data mining process for discovering groups and identifying interesting distributions and patterns in the underlying data. One of the techniques of data clustering was performed by introducing a clustering attribute. Soft set theory, initiated by Molodtsov in 1999, is a new general mathematical tool for dealing with uncertainties. In this paper, we define a soft set model on the equivalence classes of an information system, which can be easily applied in obtaining approximate sets of rough sets. Furthermore, we use it to select a clustering attribute for categorical datasets and a heuristic algorithm is presented. Experiment results on fifteen UCI benchmark datasets showed that the proposed approach provides a faster decision in selecting a clustering attribute as compared with maximum dependency attributes (MDAs) approach up to 14.84%. Furthermore, MDA and NSS have a good scalability i.e. the executing time of both algorithms tends to increase linearly as the number of instances and attributes are increased, respectively. Elsevier 2012 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/6188/1/fskkp-2012-jasni-novel_soft_set_approach_abs_only.pdf Qin, Hongwu and Ma, Xiuqin and Jasni, Mohamad Zain and Herawan, Tutut (2012) A Novel Soft Set Approach in Selecting Clustering Attribute. Knowledge-Based Systems, 36. pp. 139-145. ISSN 0950-7051 http://dx.doi.org/10.1016/j.knosys.2012.06.001 DOI: 10.1016/j.knosys.2012.06.001
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Qin, Hongwu Ma, Xiuqin Jasni, Mohamad Zain Herawan, Tutut A Novel Soft Set Approach in Selecting Clustering Attribute
description	Clustering is one of the most useful tasks in data mining process for discovering groups and identifying interesting distributions and patterns in the underlying data. One of the techniques of data clustering was performed by introducing a clustering attribute. Soft set theory, initiated by Molodtsov in 1999, is a new general mathematical tool for dealing with uncertainties. In this paper, we define a soft set model on the equivalence classes of an information system, which can be easily applied in obtaining approximate sets of rough sets. Furthermore, we use it to select a clustering attribute for categorical datasets and a heuristic algorithm is presented. Experiment results on fifteen UCI benchmark datasets showed that the proposed approach provides a faster decision in selecting a clustering attribute as compared with maximum dependency attributes (MDAs) approach up to 14.84%. Furthermore, MDA and NSS have a good scalability i.e. the executing time of both algorithms tends to increase linearly as the number of instances and attributes are increased, respectively.
format	Article
author	Qin, Hongwu Ma, Xiuqin Jasni, Mohamad Zain Herawan, Tutut
author_facet	Qin, Hongwu Ma, Xiuqin Jasni, Mohamad Zain Herawan, Tutut
author_sort	Qin, Hongwu
title	A Novel Soft Set Approach in Selecting Clustering Attribute
title_short	A Novel Soft Set Approach in Selecting Clustering Attribute
title_full	A Novel Soft Set Approach in Selecting Clustering Attribute
title_fullStr	A Novel Soft Set Approach in Selecting Clustering Attribute
title_full_unstemmed	A Novel Soft Set Approach in Selecting Clustering Attribute
title_sort	novel soft set approach in selecting clustering attribute
publisher	Elsevier
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/6188/ http://umpir.ump.edu.my/id/eprint/6188/ http://umpir.ump.edu.my/id/eprint/6188/ http://umpir.ump.edu.my/id/eprint/6188/1/fskkp-2012-jasni-novel_soft_set_approach_abs_only.pdf
first_indexed	2023-09-18T22:01:45Z
last_indexed	2023-09-18T22:01:45Z
_version_	1777414444885737472

A Novel Soft Set Approach in Selecting Clustering Attribute

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