A macro-DAG structure based mixture model. (English) Zbl 1487.62068
Summary: In the context of unsupervised classification of multidimensional data, we revisit the classical mixture model in the case where the dependencies among the random variables are described by a DAG structure. This structure is considered at two levels, the original DAG and its macro-representation. This two-level representation is the main base of the proposed mixture model. To perform unsupervised classification, we propose a dedicated algorithm called EM-mDAG, which extends the classical EM algorithm. In the Gaussian case, we show that this algorithm can be efficiently implemented. This approach has two main advantages. It favors the selection of a small number of classes and it allows a semantic interpretation of the classes based on a clustering within the macro-variables.
MSC:
| 62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
| 62F15 | Bayesian inference |
| 68T37 | Reasoning under uncertainty in the context of artificial intelligence |
Keywords:
mixture model; DAG structure; Bayesian network; EM algorithm; class selection; semantic clusteringReferences:
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