Pimwadee Chaovalit
ABSTRACT
This paper describes a novel method for clustering single and multi-dimensional data streams. With incremental computation of the incoming data, our method determines if the cluster formation should change from an initial cluster formation. Four main types of cluster evolutions are studied: cluster appearance, cluster disappearance, cluster splitting, and cluster merging. We present experimental results of our algorithms both in terms of scalability and cluster quality, compared with recent work in this area.
- Aggarwal, C. C., Han, J., Wang, J., and Yu, P. S., A framework for projected clustering of high dimensional data streams, In Proceedings of the 30th International Conference on Very Large Data Bases (Toronto, Canada). 852--863. Google Scholar
Digital Library
- Guha, S., Meyerson, A., Mishra, N., Motwani, R., and O'Callaghan, L., Clustering data streams: Theory and practice IEEE Transactions on Knowledge and Data Engineering, 15, 515--528. Google ScholarDigital Library
- Rodrigues, P. P., Gama, J., and Pedroso, J. P., Hierarchical clustering of time-series data streams IEEE Transactions on Knowledge and Data Engineering, 20, 615--627. Google ScholarDigital Library
- Udommanetanakit, K., Rakthanmanon, T., and Waiyamai, K., E-Stream: evolution-based technique for stream clustering, In Proceedings of the 3rd International Conference on Advanced Data Mining and Applications (Harbin, China, August 6--8, 2007). Springer Berlin / Heidelberg, 605--615. Google ScholarDigital Library
Index Terms
- A method for clustering transient data streams
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