Clustering data streams: theory and practice

Abstract

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.

Keywords

Computer scienceData stream miningCluster analysisData miningData streamData stream clusteringSTREAMSProcess (computing)CURE data clustering algorithmMachine learningFuzzy clusteringComputer network

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Publication Info

Year: 2003
Type: article
Volume: 15
Issue: 3
Pages: 515-528
Citations: 896
Access: Closed

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APA Style

                            
                                
                                    Suvajyoti Guha, 
                                
                                    Adam Meyerson, 
                                
                                    Nita Mishra
                                
                                et al.
                            
                            (2003). 
                            Clustering data streams: theory and practice. 
                            IEEE Transactions on Knowledge and Data Engineering
                            , 15
                            (3)
                            , 515-528.
                            https://doi.org/10.1109/tkde.2003.1198387
                        

Identifiers

DOI: 10.1109/tkde.2003.1198387