A Survey of Clustering Methods in Mining Data Streaming

The research to data streaming model has recently gained a high attraction due to its applications, including real-time surveillance systems, network intrusion detection and click streams. Clustering, one of the most important problems in streaming mining, has recently been highly explored because its application to data summarization and outlier detection. Due to the characteristics of data streaming against traditional data mining technique, new requirements and challenges have been proposed. This paper is a survey of various kinds of clustering methods in mining data streaming. In this paper, we’ll make an effort to review the state-of-the-art of clustering methods of data streaming mining and provide a big picture of this domain. To achieve this goal, we’ll first introduce the basic concepts, requirements and fundamental techniques. Then, we’ll look back into history to track the development of the clustering methods. After describing some classic and popular clustering algorithms, we’ll discuss what problems have already been solved. At last, we’ll put forward some further research issues in this domain.