  The data stream model has been defined for new classes of applications
involving massive data being generated at a fast pace. Web click stream
analysis and detection of network intrusions are two examples. Cluster analysis
on data streams becomes more difficult, because the data objects in a data
stream must be accessed in order and can be read only once or few times with
limited resources. Recently, a few clustering algorithms have been developed
for analyzing numeric data streams. However, to our knowledge to date, no
algorithm exists for clustering categorical data streams. In this paper, we
propose an efficient clustering algorithm for analyzing categorical data
streams. It has been proved that the proposed algorithm uses small memory
footprints. We provide empirical analysis on the performance of the algorithm
in clustering both synthetic and real data streams
