Mixture Model Clustering Using Variable Data Segmentation and Model Selection: A Case Study of Genetic Algorithm
Maruf GÖGEBAKAN, Hamza Erol · Mathematics Letters · 2019
A genetic algorithm for mixture model clustering using variable data segmentation and model selection is proposed in this study. Principle of the method is demonstrated on mixture model clustering of Ruspini data set. The segment numbers of the variables in the data set were determined and the variables were converted into categorical variables. It is shown that variable data segmentation forms the number and structure of cluster centers in data. Genetic Algorithms were used to determine the number of finite mixture models. The number of total mixture models and possible candidate mixture models among them are calculated using cluster centers formed by variable data segmentation in data set. Mixture of normal distributions is used in mixture model clustering. Maximum likelihood, AIC and BIC values were obtained by using the parameters in the data for each candidate mixture model. Candidate mixture models are established, to determine the number and structure of clusters, using sample means and variance-covariance matrices for data set. The best mixture model for model based clustering of data is selected according to information criteria among possible candidate mixture models. The number of components in the best mixture model corresponds to the number of clusters, and the components of the best mixture model correspond to the structure of clusters in data set.