@article{(Open Science Index):https://publications.waset.org/pdf/12873,
	  title     = {Initializing K-Means using Genetic Algorithms},
	  author    = {Bashar Al-Shboul and  Sung-Hyon Myaeng},
	  country	= {},
	  institution	= {},
	  abstract     = {K-Means (KM) is considered one of the major
algorithms widely used in clustering. However, it still has some
problems, and one of them is in its initialization step where it is
normally done randomly. Another problem for KM is that it
converges to local minima. Genetic algorithms are one of the
evolutionary algorithms inspired from nature and utilized in the field
of clustering. In this paper, we propose two algorithms to solve the
initialization problem, Genetic Algorithm Initializes KM (GAIK) and
KM Initializes Genetic Algorithm (KIGA). To show the effectiveness
and efficiency of our algorithms, a comparative study was done
among GAIK, KIGA, Genetic-based Clustering Algorithm (GCA),
and FCM [19].},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {3},
	  number    = {6},
	  year      = {2009},
	  pages     = {1481 - 1485},
	  ee        = {https://publications.waset.org/pdf/12873},
	  url   	= {https://publications.waset.org/vol/30},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 30, 2009},
	}