Clustering on demand for multiple data streams

Bi Ru Dai, Jen-Wei Huang, Mi Yen Yeh, Ming Syan Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Citations (Scopus)


In the data stream environment, the patterns generated by the mining techniques are usually distinct at different time because of the evolution of data. In order to deal with various types of multiple data streams and to support flexible mining requirements, we devise in this paper a Clustering on Demand framework, abbreviated as COD framework, to dynamically cluster multiple data streams. While providing a general framework of clustering on multiple data streams, the COD framework has two major features, namely one data scan for online statistics collection and compact multi-resolution approximations, which are designed to address, respectively, the time and the space constraints in a data stream environment. Furthermore, with the multi-resolution approximations of data streams, flexible clustering demands can be supported.

Original languageEnglish
Title of host publicationProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
EditorsR. Rastogi, K. Morik, M. Bramer, X. Wu
Number of pages4
Publication statusPublished - 2004
EventProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 - Brighton, United Kingdom
Duration: 2004 Nov 12004 Nov 4


OtherProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
Country/TerritoryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • Engineering(all)


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