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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/1200/1/0810ijcsit14.pdf
http://umpir.ump.edu.my/id/eprint/1200/
http://airccse.org/
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spelling my.ump.umpir.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
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/1200/1/0810ijcsit14.pdf
http://umpir.ump.edu.my/id/eprint/1200/
http://airccse.org/
_version_ 1643664349349281792
score 13.160551

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