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ABSTRACT
<p><b>Abstract</b>—The data stream model has recently attracted attention for its applicability to numerous types of data, including telephone records, Web documents, and clickstreams. For analysis of such data, the ability to process the data in a single pass, or a small number of passes, while using little memory, is crucial. We describe such a streaming algorithm that effectively clusters large data streams. We also provide empirical evidence of the algorithm's performance on synthetic and real data streams.</p>
INDEX TERMS
Clustering, data streams, approximation algorithms.
CITATION
Sudipto Guha, Rajeev Motwani, Nina Mishra, Liadan O'Callaghan, Adam Meyerson, "Clustering Data Streams: Theory and Practice", IEEE Transactions on Knowledge & Data Engineering, vol. 15, no. , pp. 515-528, May/June 2003, doi:10.1109/TKDE.2003.1198387
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