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The Scientific World Journal
Volume 2015 (2015), Article ID 107650, 5 pages
http://dx.doi.org/10.1155/2015/107650
Research Article

Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence

1Department of Computer Applications, Gnanamani College of Technology, AK Samuthiram, Pachal, Namakkal District, Tamil Nadu 637 018, India
2Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu 638 052, India

Received 13 April 2015; Accepted 21 April 2015

Academic Editor: Venkatesh Jaganathan

Copyright © 2015 Thenmozhi Srinivasan and Balasubramanie Palanisamy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM), with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets.