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

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Summary

Methods

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

INTRODUCTION
K-Means Clustering
ISODATA
Heredity Algorithm
Real Numerical Value GA
PROPOSED CLUSTERING METHOD
Partial Mean Distance
Selection of Fitness Value Function
Set-up Parametersfor RCGA
Performance Evaluation Method
Experiemnt 2
Experiemnt 3
Findings
CONCLUSION

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