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A mixture model-based on-line CEM algorithm
Allou Same, Gérard Govaert and Christophe Ambroise
In: Advances in Intelligent Data Analysis, 6th International Symposium on Data Analysis, IDA 2005, 8-10 Oct 2005, Madrid, Spain.
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. [Edit]
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