Clustering high dimensional data using subspace and projected clustering algorithms

Details Clustering high dimensional data using subspace and projected clustering algorithms Clustering high dimensional data using subspace and projected clustering algorithms

2010-09-02

arxiv.org/abs/1009.0384v1

International journal of computer science & information Technology (IJCSIT) Vol.2, No.4, August 2010, p.162-170

Computer Science

Databases

9 pages, 6 figures

Scientific paper

10.5121/ijcsit.2010.2414

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.

Affiliated with

Also associated with

No associations

Rating

Clustering high dimensional data using subspace and projected clustering algorithms does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Clustering high dimensional data using subspace and projected clustering algorithms, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Clustering high dimensional data using subspace and projected clustering algorithms will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-375816