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.

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Rahmat Widia Sembiring, Jasni Mohamad Zain, Abdullah Embong
• Keywords: CORR 2010, Education, Pattern Recognition, Problem Statement, PROCLUS