Genetic Algorithm in spatial clustering
Clustering has been utilized a great deal in statistical data mining as well as machine learning in case of unsupervised learning. Clustering is a centeral task in knowledge discovery and data mining. One of the limitations that clustering methods usually encounter is prede ned number of clusters. The method that is going to be presented in this paper is not constrained by such a limitation. Genetic algorithm with variable length chromosomes allows the system to pick up a needed number of clusters to successfully partition an heterogeneous data set into groups of non exclusive, more homogeneous charactersistics. The genetic algorithm utilizes the Finite Mixture Density as its objective function. Tournament selection is used to select the best tness. 2-point crossover and mutation help exploit and explore the search space.