## Abstracthe classification EM algorithm (CEM), applied using mixture models is a very useful clustering algorithm which generalize the well known k-means algorithm. This algorithm, in a practical point of view, is faster that EM algorithm and converges in a few iterations. Many actual applications require massive data sets to be classified in a real time. In that context, CEM algorithm has been used in our application dealing with a real-time flaw diagnosis for pressurized containers using acoustic emissions. Particularly, CEM algorithm has been used for the clustering of acoustic emissions whose size increase in the time. However, when the number of acoustic emissions is more than 10000, CEM algorithm has not been able to react in real time. In this work, we aim to develop an on-line mixture model based clustering algorithm.
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