This is a preview of subscription content, log in via an institution to check access.
Recommended Reading
Aggarwal, C., Han, J., Wang, J., & Yu, P. (2003). A framework for clustering evolving data streams. In Proceedings of the 29th international conference on very large data bases (pp. 81–92). San Mateo, MA: Morgan Kaufmann.
Domingos, P., & Hulten, G. (2001). A general method for scaling up machine learning algorithms and its application to clustering. In Proceedings of international conference on machine learning (pp. 106–113). San Mateo, MA: Morgan Kaufmann.
Farnstrom, F., Lewis, J., & Elkan, C. (2000). Scalability for clustering algorithms revisited. SIGKDD Explorations, 2(1), 51–57.
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, 15(3), 515–528.
Spiliopoulou, M., Ntoutsi, I., Theodoridis, Y., & Schult, R. (2006). Monic: Modeling and monitoring cluster transitions. In Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (pp. 706–711). New York: ACM Press.
Zhang, T., Ramakrishnan, R., & Livny, M. (1996). Birch: An efficient data clustering method for very large databases. In Proceedings of ACM SIGMOD international conference on management of data (pp. 103–114). New York: ACM Press.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this entry
Cite this entry
Gama, J. (2011). Clustering from Data Streams. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_127
Download citation
DOI: https://doi.org/10.1007/978-0-387-30164-8_127
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30768-8
Online ISBN: 978-0-387-30164-8
eBook Packages: Computer ScienceReference Module Computer Science and Engineering