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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Academy & Industry Research Collaboration Center (AIRCC)
2010
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| 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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| author | Sembiring, Rahmat Widia Jasni, Mohamad Zain Abdullah, Embong |
| author_facet | Sembiring, Rahmat Widia Jasni, Mohamad Zain Abdullah, Embong |
| author_sort | Sembiring, Rahmat Widia |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.ump.umpir.1200 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2010 |
| publisher | Academy & Industry Research Collaboration Center (AIRCC) |
| record_format | eprints |
| 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) . (Published) http://airccse.org/ DOI : 10.5121/ijcsit.2010.2414 |
| spellingShingle | QA75 Electronic computers. Computer science Sembiring, Rahmat Widia Jasni, Mohamad Zain Abdullah, Embong Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms |
| title | Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms
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| title_full | Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms
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| title_fullStr | Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms
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| title_full_unstemmed | Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms
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| title_short | Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms
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| title_sort | clustering high dimensional data using subspace and projected clustering algorithms |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/1200/1/0810ijcsit14.pdf http://umpir.ump.edu.my/id/eprint/1200/ http://airccse.org/ |
| url_provider | http://umpir.ump.edu.my/ |