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.

Adel A. Sewisy, M. H. Marghny, Rasha M. Abd ElAziz and Ahmed I. Taloba, “Fast Efficient Clustering Algorithm for Balanced Data” International Journal of Advanced Computer Science and Applications(IJACSA), 5(6), 2014. http://dx.doi.org/10.14569/IJACSA.2014.050619

@article{Sewisy2014,
title = {Fast Efficient Clustering Algorithm for Balanced Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2014.050619},
url = {http://dx.doi.org/10.14569/IJACSA.2014.050619},
year = {2014},
publisher = {The Science and Information Organization},
volume = {5},
number = {6},
author = {Adel A. Sewisy and M. H. Marghny and Rasha M. Abd ElAziz and Ahmed I. Taloba}
}