Author(s)

Ahamed Al Malki¹, Mohamed M. Rizk^1,2, M. A. El-Shorbagy³, A. A. Mousa^1,3*

¹Department of Mathematics and Statistic, Faculty of Science, Taif University, Taif, Saudi Arabia.
²Department of Mathematics, Faculty of Science, Menoufia University, Al Minufya, Egypt.
³Department of Basic engineering sciences, Faculty of Engineering, Menoufia University, Al Minufya, Egypt.

The K-means method is one of the most widely used clustering methods and has been implemented in many fields of science and technology. One of the major problems of the k-means algorithm is that it may produce empty clusters depending on initial center vectors. Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary principles of natural selection and genetics. This paper presents a hybrid version of the k-means algorithm with GAs that efficiently eliminates this empty cluster problem. Results of simulation experiments using several data sets prove our claim.

Cluster Analysis, Genetic Algorithm, K-Means

Malki, A. , Rizk, M. , El-Shorbagy, M. and Mousa, A. (2016) Hybrid Genetic Algorithm with K-Means for Clustering Problems. Open Journal of Optimization, 5, 71-83. doi: 10.4236/ojop.2016.52009.