Based Density Data Stream Cluster Mining Algorithm

Recently, there are more and more applications that are facing the envirnoment of stream data. Stream data is a kind of continuous; ordered, changing fast and huge amount data. It is quite a new object that is different from traditional static data stored on the disk. Currently, data mining in data stream becomes a hot research field. First, we introduce the knowledge of data mining and discuss the data stream mining, then we build a data stream mining algorithm—DSCluster which may cluster and detect outliers in data stream containing both continuous and categorical attributes. Furthermore, the paper reports experiments on real-life datasets and synthetic datasets, the results show that our algorithm can get higher accuracy of clustering within limited memory, and has the good scalability with the quantity and the dimensionality of stream data. Finally, we summarize the content of paper and point out the research emphases for future work.