Clustering on demand for multiple data streams

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

研究成果: Conference contribution

23 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
編輯R. Rastogi, K. Morik, M. Bramer, X. Wu
頁面367-370
頁數4
出版狀態Published - 2004
事件Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 - Brighton, United Kingdom
持續時間: 2004 十一月 12004 十一月 4

Other

OtherProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
國家/地區United Kingdom
城市Brighton
期間04-11-0104-11-04

All Science Journal Classification (ASJC) codes

  • 工程 (全部)

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