Abstract: Many current and emerging applications require support for on-line analysis of rapidly changing data streams. Limitations of traditional DI3MSs and data mining in supporting streaming applications have been recognized,prompting research to augment existing technologies and build new systems to manage streaming data and propose new algorithm for mining data stream A synopsis data structure based mixture probabilistic density data stream clustering approach was proposed,which rectuires only the newly arrived data,not the entire historical data,to be saved in memory. This approach incrementally updates the density estimate taking only the newly arrived data and the previously estimated density. I}his method uses three distance metric criteria for judging if merging new arriving component into a component of existing Gaussian mixture model or as a new model is added existing Uaussian mixture model. The experimental results have demonstrated that the algorithm is feasible and fulfill high quality clustering results.

Key words: Data stream,Mixture model clustering,Patterns