@inproceedings{Cao2006,
	title = {Density-based clustering over an evolving data stream with noise},
	author = {Feng Cao and Martin Ester and Weining Qian and Aoying Zhou},
	booktitle = {SIAM International Conference on Data Mining},
	url = {http://www.siam.org/meetings/sdm06/proceedings/030caof.pdf},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/21b9a0417d8d86afe1c42d0018ff2e928/marcoalvarez},
	abstract = {Clustering is an important task in mining evolving data streams. Beside
	the limited memory and one-pass constraints, the nature of evolving
	data streams implies the following requirements for stream clustering:
	no assumption on the number of clusters, discovery of clusters with
	arbitrary shape and ability to handle outliers. While a lot of clustering
	algorithms for data streams have been proposed, they offer no solution
	to the combination of these requirements. In this paper, we present
	DenStream, a new approach for discovering clusters in an evolving
	data stream. The �dense� micro-cluster (named core-micro-cluster)
	is introduced to summarize the clusters with arbitrary shape, while
	the potential core-micro-cluster and outlier micro-cluster structures
	are proposed to maintain and distinguish the potential clusters and
	outliers. A novel pruning strategy is designed based on these concepts,
	which guarantees the precision of the weights of the micro-clusters
	with limited memory. Our performance study over a number of real
	and synthetic data sets demonstrates the effectiveness and efficiency
	of our method.},
	keywords = {Clustering DataStream }
}