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

Ahmed Najjar, Christian Gagné and Daniel Reinharz

Abstract - 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 Quebec, with results illustrating the good performances of the proposed method.

Bibtex:

@inproceedings{Najjar1122,
author    = { Ahmed Najjar and Christian Gagné and Daniel Reinharz },
title     = { Two-Step Heterogeneous Finite Mixture Model Clustering for Mining Healthcare Databases },
booktitle = { Proc. of the IEEE International Conference on Data Mining (ICDM) },
year      = { 2015 },
month     = { 11 },
location  = { Atlantic City, NJ, USA }
}

Dernière modification: 2015/09/27 par cgagne

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