Many applications such as news group filtering, text crawling, and document organization require real time clustering and segmentation of text data records. The categorical data stream clustering problem also has a number of applications to the problems of customer segmentation and real time trend analysis. We will present an online approach for clustering massive text and categorical data streams with the use of a statistical summarization methodology. We present results illustrating the effectiveness of the technique.
A recent technique REF was designed to cluster both text and categorical data streams.
A Framework for Clustering Massive Text and Categorical Data Streams
IN: PROC. SIAM CONFERENCE ON DATA MINING
Computer Science