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Research Article | Open Access |

Fast Efficient Clustering Algorithm for Balanced Data

Author 1: Adel A. Sewisy Author 2: M. H. Marghny Author 3: Rasha M. Abd ElAziz Author 4: Ahmed I. Taloba
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 5, No. 6 · Published 2014 · Cited by 10

DOI: https://doi.org/10.14569/IJACSA.2014.050619

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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.

Keywords

Clustering K-means algorithm Bee algorithm GA algorithm FBK-means algorithm

How to Cite this Article

Sewisy, A. A., Marghny, M. H., ElAziz, R. M. A., & Taloba, A. I. (2014). Fast Efficient Clustering Algorithm for Balanced Data. International Journal of Advanced Computer Science and Applications, 5(6). https://doi.org/10.14569/IJACSA.2014.050619

Sewisy, Adel A., et al.. "Fast Efficient Clustering Algorithm for Balanced Data." International Journal of Advanced Computer Science and Applications, vol. 5, no. 6, 2014, https://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},
  volume    = {5},
  number    = {6},
  year      = {2014},
  publisher = {The Science and Information Organization},
  author    = {Adel A. Sewisy and M. H. Marghny and Rasha M. Abd ElAziz and Ahmed I. Taloba},
  doi       = {10.14569/IJACSA.2014.050619},
  url       = {https://doi.org/10.14569/IJACSA.2014.050619}
}

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