Clustering high dimensional data using subspace and projected clustering algorithms
Abstract
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.
Cite
@article{arxiv.1009.0384,
title = {Clustering high dimensional data using subspace and projected clustering algorithms},
author = {Rahmat Widia Sembiring and Jasni Mohamad Zain and Abdullah Embong},
journal= {arXiv preprint arXiv:1009.0384},
year = {2010}
}
Comments
9 pages, 6 figures