Abstract
A method of GA: Genetic Algorithm based ISODATA clustering is proposed.GA clustering is now widely available. One of the problems for GA clustering is a poor clustering performance due to the assumption that clusters are represented as convex functions. Well known ISODATA clustering has parameters of threshold for merge and split. The parameters have to be determined without any assumption (convex functions). In order to determine the parameters, GA is utilized. Through comparatives studies between with and without parameter estimation with GA utilizing well known UCI Repository data clustering performance evaluation, it is found that the proposed method is superior to the original ISODATA and also the other conventional clustering methods.
Highlights
MethodsAbstract— A method of GA: Genetic Algorithm based ISODATA clustering is proposed.GA clustering is widely available
Clustering is the method of collecting the comrades of each-other likeness, making a group based on the similarity and dissimilarity nature between object individuals, and classifying an object in the heterogeneous object of a thing [1]
The proposed clustering method is based on the conventional ISODAT method
Summary
Abstract— A method of GA: Genetic Algorithm based ISODATA clustering is proposed.GA clustering is widely available. One of the problems for GA clustering is a poor clustering performance due to the assumption that clusters are represented as convex functions. Well known ISODATA clustering has parameters of threshold for merge and split. The parameters have to be determined without any assumption (convex functions). In order to determine the parameters, GA is utilized. Through comparatives studies between with and without parameter estimation with GA utilizing well known UCI Repository data clustering performance evaluation, it is found that the proposed method is superior to the original ISODATA and the other conventional clustering methods
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More From: International Journal of Advanced Computer Science and Applications
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