Clustering using genetic algorithm combining validation criteria.
Murilo Coelho Naldi, André C. P. L. F. de Carvalho · 2007
Abstract. Clustering techniques have been a valuable tool for several data analysis applications. However, one of the main difficulties associated with clustering is the validation of the results obtained. Both clustering algorithms and validation criteria present an inductive bias, which can favor datasets with particular characteristics. Besides, different runs of the same algorithm using the same data set may produce different clusters. In this work, traditional clustering and validation techniques are combined with a Genetic Algorithm (GA) to build clusters that better approximate the real distribution of the dataset. The GA employs a fitness function that combines two validation criteria. Such combination allows the GA to improve the evaluation of the candidate solutions. Furthermore, this combined approach avoids the individual weaknesses of each criterion. A set of experiments are run to compare the proposed model with other clustering algorithms, with promising results. 1