Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms

Sembiring, Rahmat Widia and Jasni, Mohamad Zain and Abdullah, Embong (2010) Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms. International journal of computer science & information Technology (IJCSIT), Vol.2 (No.4, ). pp. 162-170. ISSN 0975-3826(online); 0975-4660 (Print) . (Published)

0810ijcsit14.pdf - Published Version

Download (2MB)
DOI/Official URL:


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 analyse in detail the properties of different data clustering method.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Mr. Rahmat Widia Sembiring
Date Deposited: 25 Apr 2011 03:14
Last Modified: 22 May 2018 02:39
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item