Two-Step Heterogeneous Finite Mixture Model Clustering for Mining Healthcare Databases

Dealing with real-life databases often implies handling sets of heterogeneous variables. We are proposing in this paper a methodology for exploring and analyzing such databases, with an application in the specific domain of healthcare data analytics. We are thus proposing a two-step heterogeneous finite mixture model, with a first step involving a joint mixture of Gaussian and multinomial distribution to handle numerical (i.e., real and integer numbers) and categorical variables (i.e., discrete values), and a second step featuring a mixture of hidden Markov models to handle sequences of categorical values (e.g., series of events). This approach is evaluated on a real-world application, the clustering of administrative healthcare databases from Québec, with results illustrating the good performances of the proposed method.