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

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...

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Bibliographic Details
Published in:TheScientificWorld 2015, Vol.2015 (2015), p.1-5
Main Authors: Srinivasan, Thenmozhi, Palanisamy, Balasubramanie
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Clustering
Data mining
Datasets
Gene expression
Intelligence
Mathematical optimization
Methods
Optimization techniques
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Summary: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.
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2015/107650