Comparative Study between the Proposed GA Based ISODAT Clustering and the Conventional Clustering Methods
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2012, Vol 3, Issue 7
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.
Authors and Affiliations
Kohei Arai
Keywords
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