Abstract
Fisher distance based Genetic Algorithm: GA clustering method which takes into account overlapped space among probability density functions of clusters in feature space is proposed. Through experiments with simulation data of 2D and 3D feature space generated by random number generator, it is found that clustering performance depends on overlapped space among probability density function of clusters. Also it is found relation between cluster performance and the GA parameters, crossover and mutation probability as well as the number of features and the number of clusters.
Highlights
Genetic Algorithm: GA clustering is widely used for image clustering
The following section describes fundamental theoretical background of the Fisher distance based GA clustering method followed by some experimental results with simulation data
Due to tha fact that allowable residual error is set at 5% as convergence condition, cluster performance of the SA based clustering is 98%
Summary
Genetic Algorithm: GA clustering is widely used for image clustering. It allows relatively good clustering performance with marginal computer resources. Fisher distance based GA clustering is well known [1]. It is not clear the characteristics of Fisher distance based GA clustering. Relation between clustering performance and overlapped space among probability density function of clusters. Relation between cluster performance and the GA parameters, crossover and mutation probability as well as the number of features and the number of clusters are unclear [2]. The following section describes fundamental theoretical background of the Fisher distance based GA clustering method followed by some experimental results with simulation data. Conclusion and remarks are described together with some discussions
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