@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},
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 }
}
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