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Clustering is a process of putting similar data into groups. This

paper presents data clustering using improved genetic algorithm

(IGA) in which an efficient method of crossover and mutation are

implemented. Further it is hybridized with the popular Nelder-

Mead (NM) Simplex search and K-means to exploit the

potentiality of both in the hybridized algorithm. The performance

of hybrid approach is evaluated with few data clustering

problems. Further a Variable Length IGA is proposed which

optimally finds the clusters of benchmark image datasets and the

performance is compared with K-means and GCUK[12].The

results revealed are very encouraging with IGA and its

hybridization with other algorithms

Clustering is a process of putting similar data into groups. This

paper presents data clustering using improved genetic algorithm

(IGA) in which an efficient method of crossover and mutation are

implemented. Further it is hybridized with the popular Nelder-

Mead (NM) Simplex search and K-means to exploit the

potentiality of both in the hybridized algorithm. The performance

of hybrid approach is evaluated with few data clustering

problems. Further a Variable Length IGA is proposed which

optimally finds the clusters of benchmark image datasets and the

performance is compared with K-means and GCUK[12].The

results revealed are very encouraging with IGA and its

hybridization with other algorithms

Clustering is a process of putting similar data into groups. Thispaper presents data clustering using improved genetic algorithm(IGA) in which an efficient method of crossover and mutation are implemented. Further it is hybridized with the popular Nelder-Mead (NM) Simplex search and K-means to exploit the potentiality of both in the hybridized algorithm. The performanceof hybrid approach is evaluated with few data clusteringproblems. Further a Variable Length IGA is proposed whichoptimally

finds the clusters of benchmark image datasets and theperformance is compared with K-means and GCUK[12].The results revealed are very encouraging with IGA and

its hybridization with other algorithms

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