  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.
