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CORR
2010
Springer

Clustering high dimensional data using subspace and projected clustering algorithms

8 years 4 months ago
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 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 analyze in detail the properties of different data clustering method.
Rahmat Widia Sembiring, Jasni Mohamad Zain, Abdull
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2010
Where CORR
Authors Rahmat Widia Sembiring, Jasni Mohamad Zain, Abdullah Embong
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