@article{(Open Science Index):https://publications.waset.org/pdf/12779,
	  title     = {K-Means for Spherical Clusters with Large Variance in Sizes},
	  author    = {A. M. Fahim and  G. Saake and  A. M. Salem and  F. A. Torkey and  M. A. Ramadan},
	  country	= {},
	  institution	= {},
	  abstract     = {Data clustering is an important data exploration
technique with many applications in data mining. The k-means
algorithm is well known for its efficiency in clustering large data
sets. However, this algorithm is suitable for spherical shaped clusters
of similar sizes and densities. The quality of the resulting clusters
decreases when the data set contains spherical shaped with large
variance in sizes. In this paper, we introduce a competent procedure
to overcome this problem. The proposed method is based on shifting
the center of the large cluster toward the small cluster, and recomputing
the membership of small cluster points, the experimental
results reveal that the proposed algorithm produces satisfactory
results.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {2},
	  number    = {9},
	  year      = {2008},
	  pages     = {2923 - 2928},
	  ee        = {https://publications.waset.org/pdf/12779},
	  url   	= {https://publications.waset.org/vol/21},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 21, 2008},
	}