Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence
Joint Authors
Srinivasan, Thenmozhi
Palanisamy, Balasubramanie
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-01
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Srinivasan, Thenmozhi& Palanisamy, Balasubramanie. 2015. Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence. The Scientific World Journal،Vol. 2015, no. 2015, pp.1-5.
https://search.emarefa.net/detail/BIM-1078470
Modern Language Association (MLA)
Srinivasan, Thenmozhi& Palanisamy, Balasubramanie. Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence. The Scientific World Journal No. 2015 (2015), pp.1-5.
https://search.emarefa.net/detail/BIM-1078470
American Medical Association (AMA)
Srinivasan, Thenmozhi& Palanisamy, Balasubramanie. Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence. The Scientific World Journal. 2015. Vol. 2015, no. 2015, pp.1-5.
https://search.emarefa.net/detail/BIM-1078470
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1078470