TITLE:
Hybrid Genetic Algorithm with K-Means for Clustering Problems
AUTHORS:
Ahamed Al Malki, Mohamed M. Rizk, M. A. El-Shorbagy, A. A. Mousa
KEYWORDS:
Cluster Analysis, Genetic Algorithm, K-Means
JOURNAL NAME:
Open Journal of Optimization,
Vol.5 No.2,
June
21,
2016
ABSTRACT: 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.