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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.
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Cites
Finding Groups in Data: An Introduction to Cluster Analysis
Kaufman, L., & Rousseeuw, P. J. (1990) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316801 · Cited by 7,344
Ankerst, M. (1999) · doi:10.1145/304181.304187 · Cited by 3,982
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(2007) · doi:10.5555/1283383.1283494 · Cited by 7,364
A Comprehensive Survey of Clustering Algorithms
Xu, D., & Tian, Y. (2015) · Annals of Data Science · doi:10.1007/s40745-015-0040-1 · Cited by 1,939
Data Mining for Internet of Things: A Survey
Tsai, C.-W., Lai, C.-F., Chiang, M.-C., & Yang, L. T. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.103013.00206 · Cited by 620
Discrete Wavelet transform-based time series analysis and mining
Chaovalit, P., Gangopadhyay, A., Karabatis, G., & Chen, Z. (2011) · ACM Computing Surveys · doi:10.1145/1883612.1883613 · Cited by 218
Metadata
- Authors
- Guha, S., Meyerson, A., Mishra, N., Motwani, R., & O'Callaghan, L.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2003-05
- Volume
- 15
- Issue
- 3
- Pages
- 515-528
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2123297508
- Cited by
- 882
- References
- 2
- FWCI
- 26.15
- Type
- journal article
- Language
- en
- Open access
- closed
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- Guha, S., Meyerson, A., Mishra, N., Motwani, R., & O'Callaghan, L. (2003). Clustering data streams: theory and practice. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2003.1198387
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
