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K-mean method is a clustering method in which grouping techniques are based only on distance measure among observed objects, without considering statistical aspects. Model-based clustering is a method that use statistical aspects, as its theoretical basis i.e. probability maximum criterion. This model has several variations with a variety of geometrical characteristics obtained by mean Gauss component. Data partition is conducted by utilizing EM (expectation-maximization) algorithm. Then by using Bayesian Information Criterion (BIC) the best model is obtained. This research aimed to comparing result of grouping methods between model-based clustering and K-mean clustering. The results showed that model-based clustering was more effective in separating overlap groups than K-mean.
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