Clustering Data Streams Using Graphics-Based Method
A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. For analysis of such data, the ability to process the data in a single pass with high clustering quality, while using little memory, is crucial. A graphics-based method is proposed for incrementally clustering large data streams. In the method, the underlying clusters in data streams are represented by a set of graphics, and clustering is transformed into a gradual process of constructing the graphics profiles (i.e., the boundaries of clusters) in a single scan of the data. The method has been used to on-line intrusion detection, and experimental results have shown its effectiveness.