An on-line Classification EM algorithm based on mixture model

Mixture model-based clustering is widely used in many applications. In real-time applications, data are received sequentially and classification parameters have to be quickly updated. An on-line clustering algorithm based on mixture models is presented in the context of a real time flaw diagnosis application for pressurized containers. Available data for this application are acoustic emission signals. The proposed algorithm is a stochastic gradient one derived from the Classification version of the EM algorithm (CEM). It provides a model-based generalization of the well known on-line k-means algorithm to handle non spherical clusters when specific Gaussian mixture models are used. Using synthetic and real data sets, the proposed algorithm is compared to the batch CEM algorithm and the on-line EM algorithm. The three approaches generates comparable solutions in terms of the resulting partition when clusters are relatively well separated but on-line algorithms become fasters when the size of the available observations increases.