Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms

Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerates clusters of objects in all subspaces of a dataset. It tends to produce many over lapping clusters. Approach: Subspace cl...

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Main Authors:	Sembiring, Rahmat Widia, Jasni, Mohamad Zain, Abdullah, Embong
Format:	Article
Language:	English
Published:	Academy & Industry Research Collaboration Center (AIRCC) 2010
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/1200/ http://umpir.ump.edu.my/id/eprint/1200/ http://umpir.ump.edu.my/id/eprint/1200/ http://umpir.ump.edu.my/id/eprint/1200/1/0810ijcsit14.pdf

id	ump-1200
recordtype	eprints
spelling	ump-12002018-05-22T02:39:51Z http://umpir.ump.edu.my/id/eprint/1200/ Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms Sembiring, Rahmat Widia Jasni, Mohamad Zain Abdullah, Embong QA75 Electronic computers. Computer science Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerates clusters of objects in all subspaces of a dataset. It tends to produce many over lapping clusters. Approach: Subspace clustering and projected clustering are research areas for clustering in high dimensional spaces. In this research we experiment three clustering oriented algorithms, PROCLUS, P3C and STATPC. Results: In general, PROCLUS performs better in terms of time of calculation and produced the least number of un-clustered data while STATPC outperforms PROCLUS and P3C in the accuracy of both cluster points and relevant attributes found. Conclusions/Recommendations: In this study, we analyse in detail the properties of different data clustering method. Academy & Industry Research Collaboration Center (AIRCC) 2010 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1200/1/0810ijcsit14.pdf Sembiring, Rahmat Widia and Jasni, Mohamad Zain and Abdullah, Embong (2010) Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms. International journal of computer science & information Technology (IJCSIT), Vol.2 (No.4, ). pp. 162-170. ISSN 0975-3826(online); 0975-4660 (Print) http://airccse.org/ DOI : 10.5121/ijcsit.2010.2414
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 Sembiring, Rahmat Widia Jasni, Mohamad Zain Abdullah, Embong Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms
description	Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerates clusters of objects in all subspaces of a dataset. It tends to produce many over lapping clusters. Approach: Subspace clustering and projected clustering are research areas for clustering in high dimensional spaces. In this research we experiment three clustering oriented algorithms, PROCLUS, P3C and STATPC. Results: In general, PROCLUS performs better in terms of time of calculation and produced the least number of un-clustered data while STATPC outperforms PROCLUS and P3C in the accuracy of both cluster points and relevant attributes found. Conclusions/Recommendations: In this study, we analyse in detail the properties of different data clustering method.
format	Article
author	Sembiring, Rahmat Widia Jasni, Mohamad Zain Abdullah, Embong
author_facet	Sembiring, Rahmat Widia Jasni, Mohamad Zain Abdullah, Embong
author_sort	Sembiring, Rahmat Widia
title	Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms
title_short	Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms
title_full	Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms
title_fullStr	Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms
title_full_unstemmed	Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms
title_sort	clustering high dimensional data using subspace and projected clustering algorithms
publisher	Academy & Industry Research Collaboration Center (AIRCC)
publishDate	2010
url	http://umpir.ump.edu.my/id/eprint/1200/ http://umpir.ump.edu.my/id/eprint/1200/ http://umpir.ump.edu.my/id/eprint/1200/ http://umpir.ump.edu.my/id/eprint/1200/1/0810ijcsit14.pdf
first_indexed	2023-09-18T21:54:09Z
last_indexed	2023-09-18T21:54:09Z
_version_	1777413966122713088

Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms

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