Fast Efficient Clustering Algorithm for Balanced Data
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2014, Vol 5, Issue 6
Abstract
The Cluster analysis is a major technique for statistical analysis, machine learning, pattern recognition, data mining, image analysis and bioinformatics. K-means algorithm is one of the most important clustering algorithms. However, the k-means algorithm needs a large amount of computational time for handling large data sets. In this paper, we developed more efficient clustering algorithm to overcome this deficiency named Fast Balanced k-means (FBK-means). This algorithm is not only yields the best clustering results as in the k-means algorithm but also requires less computational time. The algorithm is working well in the case of balanced data.
Authors and Affiliations
Adel Sewisy, M. Marghny, Rasha ElAziz, Ahmed Taloba
Keywords
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- EP ID EP147446
- DOI 10.14569/IJACSA.2014.050619
- Views 87
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