Cluster Evaluation of Density Based Subspace Clustering

Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach. Density approaches based on the paradigm introduced by DBSCAN clustering. In this app...

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Main Authors:	Sembiring, Rahmat Widia, Jasni, Mohamad Zain
Format:	Article
Language:	English English
Published:	2010
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/1201/ http://umpir.ump.edu.my/id/eprint/1201/ http://umpir.ump.edu.my/id/eprint/1201/1/Cluster-Evaluation-of-Density-Based-Subspace-Clustering http://umpir.ump.edu.my/id/eprint/1201/2/Cluster-Evaluation-of-Density-Based-Subspace-Clustering

id	ump-1201
recordtype	eprints
spelling	ump-12012018-05-22T02:32:02Z http://umpir.ump.edu.my/id/eprint/1201/ Cluster Evaluation of Density Based Subspace Clustering Sembiring, Rahmat Widia Jasni, Mohamad Zain QA75 Electronic computers. Computer science Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach. Density approaches based on the paradigm introduced by DBSCAN clustering. In this approach, density of each object neighbours with MinPoints will be calculated. Cluster change will occur in accordance with changes in density of each object neighbours. The neighbours of each object typically determined using a distance function, for example the Euclidean distance. In this paper SUBCLU, FIRES and INSCY methods will be applied to clustering 6x1595 dimension synthetic datasets. IO Entropy, F1 Measure, coverage, accurate and time consumption used as evaluation performance parameters. Evaluation results showed SUBCLU method requires considerable time to process subspace clustering; however, its value coverage is better. Meanwhile INSCY method is better for accuracy comparing with two other methods, although consequence time calculation was longer. 2010 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1201/1/Cluster-Evaluation-of-Density-Based-Subspace-Clustering other en http://umpir.ump.edu.my/id/eprint/1201/2/Cluster-Evaluation-of-Density-Based-Subspace-Clustering Sembiring, Rahmat Widia and Jasni, Mohamad Zain (2010) Cluster Evaluation of Density Based Subspace Clustering. Journal of Computing, Volume 2, (Issue 11, November 2010). pp. 14-19. ISSN 2151-9617 http://umpir.ump.edu.my/1201/1/Cluster-Evaluation-of-Density-Based-Subspace-Clustering
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Sembiring, Rahmat Widia Jasni, Mohamad Zain Cluster Evaluation of Density Based Subspace Clustering
description	Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach. Density approaches based on the paradigm introduced by DBSCAN clustering. In this approach, density of each object neighbours with MinPoints will be calculated. Cluster change will occur in accordance with changes in density of each object neighbours. The neighbours of each object typically determined using a distance function, for example the Euclidean distance. In this paper SUBCLU, FIRES and INSCY methods will be applied to clustering 6x1595 dimension synthetic datasets. IO Entropy, F1 Measure, coverage, accurate and time consumption used as evaluation performance parameters. Evaluation results showed SUBCLU method requires considerable time to process subspace clustering; however, its value coverage is better. Meanwhile INSCY method is better for accuracy comparing with two other methods, although consequence time calculation was longer.
format	Article
author	Sembiring, Rahmat Widia Jasni, Mohamad Zain
author_facet	Sembiring, Rahmat Widia Jasni, Mohamad Zain
author_sort	Sembiring, Rahmat Widia
title	Cluster Evaluation of Density Based Subspace Clustering
title_short	Cluster Evaluation of Density Based Subspace Clustering
title_full	Cluster Evaluation of Density Based Subspace Clustering
title_fullStr	Cluster Evaluation of Density Based Subspace Clustering
title_full_unstemmed	Cluster Evaluation of Density Based Subspace Clustering
title_sort	cluster evaluation of density based subspace clustering
publishDate	2010
url	http://umpir.ump.edu.my/id/eprint/1201/ http://umpir.ump.edu.my/id/eprint/1201/ http://umpir.ump.edu.my/id/eprint/1201/1/Cluster-Evaluation-of-Density-Based-Subspace-Clustering http://umpir.ump.edu.my/id/eprint/1201/2/Cluster-Evaluation-of-Density-Based-Subspace-Clustering
first_indexed	2023-09-18T21:54:09Z
last_indexed	2023-09-18T21:54:09Z
_version_	1777413966266368000

Cluster Evaluation of Density Based Subspace Clustering

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