Data stream clustering algorithm based on semi-supervised affinity propagation

In order to improve the clustering quality of evolving data stream, this paper introduces a new data stream clustering algorithm, clustering over data Stream based on Semi-supervised Affinity Propagation(SAPStream), this algorithm calculates the similarity matrix of the initial data with the idea of semi-supervised, executes AP cluster, and then builds online clustering model. With the evolution of the data stream, the clustering model adjusts using decay windows technology, and the data stream clustering results are got by executing cluster again over the exemplars and new arrival data points. SAPStream can analyze and deal with large-scale evolving data stream. Its performance is tested by using both real datasets and synthetic datasets. Experimental results show this algorithm achieves a higher quality of clustering.