@inproceedings{Dai2004,
	title = {Clustering on demand for multiple data streams},
	author = {Bi-Ru Dai and Jen-Wei Huang and Mi-Yen Yeh and Ming-Syan Chen},
	booktitle = {IEEE International Conference on Data Mining},
	pages = {367--370},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1410312},
	year = {2004},
	biburl = {http://www.bibsonomy.org/bibtex/2f3981deb0c8f80994b8de3063b6dc9e0/marcoalvarez},
	abstract = {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.},
	keywords = {Clustering DataStream }
}