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: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Academy & Industry Research Collaboration Center (AIRCC)
2010
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| Subjects: | |
| 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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| Summary: | 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. |
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