Augmentation of Densest Subgraph Finding Unsupervised Feature Selection Using Shared Nearest Neighbor Clustering

Chugh, D; Mittal, H; Saxena, A; Chauhan, R; Yafi, E; Prasad, M

Augmentation of Densest Subgraph Finding Unsupervised Feature Selection Using Shared Nearest Neighbor Clustering

Chugh, D Mittal, H Saxena, A Chauhan, R Yafi, E

Prasad, M

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Algorithms, 2023, 16, (1), pp. 28
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Chugh, D
dc.contributor.author	Mittal, H
dc.contributor.author	Saxena, A
dc.contributor.author	Chauhan, R
dc.contributor.author	Yafi, E https://orcid.org/0000-0002-3144-0784
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.date.accessioned	2023-03-24T09:33:27Z
dc.date.available	2023-03-24T09:33:27Z
dc.date.issued	2023-01-01
dc.identifier.citation	Algorithms, 2023, 16, (1), pp. 28
dc.identifier.issn	1999-4893
dc.identifier.issn	1999-4893
dc.identifier.uri	http://hdl.handle.net/10453/168352
dc.description.abstract	Determining the optimal feature set is a challenging problem, especially in an unsupervised domain. To mitigate the same, this paper presents a new unsupervised feature selection method, termed as densest feature graph augmentation with disjoint feature clusters. The proposed method works in two phases. The first phase focuses on finding the maximally non-redundant feature subset and disjoint features are added to the feature set in the second phase. To experimentally validate, the efficiency of the proposed method has been compared against five existing unsupervised feature selection methods on five UCI datasets in terms of three performance criteria, namely clustering accuracy, normalized mutual information, and classification accuracy. The experimental analyses have shown that the proposed method outperforms the considered methods.
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Algorithms
dc.relation.isbasedon	10.3390/a16010028
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.title	Augmentation of Densest Subgraph Finding Unsupervised Feature Selection Using Shared Nearest Neighbor Clustering
dc.type	Journal Article
utslib.citation.volume	16
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-24T09:33:25Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	16
utslib.citation.issue	1

Abstract:

Determining the optimal feature set is a challenging problem, especially in an unsupervised domain. To mitigate the same, this paper presents a new unsupervised feature selection method, termed as densest feature graph augmentation with disjoint feature clusters. The proposed method works in two phases. The first phase focuses on finding the maximally non-redundant feature subset and disjoint features are added to the feature set in the second phase. To experimentally validate, the efficiency of the proposed method has been compared against five existing unsupervised feature selection methods on five UCI datasets in terms of three performance criteria, namely clustering accuracy, normalized mutual information, and classification accuracy. The experimental analyses have shown that the proposed method outperforms the considered methods.

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http://hdl.handle.net/10453/168352