Hybrid Genetic Algorithm with K-Means for Clustering Problems

作     者：Ahamed Al Malki Mohamed M. Rizk M. A. El-Shorbagy A. A. Mousa Ahamed Al Malki;Mohamed M. Rizk;M. A. El-Shorbagy;A. A. Mousa

作者机构：Department of Mathematics and Statistic Faculty of Science Taif University Taif Saudi Arabia Department of Mathematics Faculty of Science Menoufia University Al Minufya Egypt Department of Basic engineering sciences Faculty of Engineering Menoufia University Al Minufya Egypt 

出 版 物：《Open Journal of Optimization》 (最优化（英文）)

年 卷 期：2016年第5卷第2期

页      面：71-83页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Cluster Analysis Genetic Algorithm K-Means 

摘      要：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.