TITLE

Initializing K-Means using Genetic Algorithms

AUTHOR(S)
Al-Shboul, Bashar; Sung-Hyon Myaeng
PUB. DATE
June 2009
SOURCE
World Academy of Science, Engineering & Technology;Jun2009, Issue 30, p114
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
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].
ACCESSION #
52895267

 

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