Density-based clustering over an evolving data stream with noise

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Density-based clustering over an evolving data stream with noise (2006)

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by Feng Cao , Martin Ester , Weining Qian , Aoying Zhou

Venue:	In 2006 SIAM Conference on Data Mining
Citations:	84 - 2 self

BibTeX

@INPROCEEDINGS{Cao06density-basedclustering,
    author = {Feng Cao and Martin Ester and Weining Qian and Aoying Zhou},
    title = {Density-based clustering over an evolving data stream with noise},
    booktitle = {In 2006 SIAM Conference on Data Mining},
    year = {2006},
    pages = {328--339}
}

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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.

Keyphrases

data stream density-based clustering limited memory arbitrary shape one-pass constraint important task performance study micro-cluster structure potential core-micro-cluster novel pruning strategy following requirement new approach potential cluster synthetic data set stream clustering evolving data stream dense micro-cluster

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