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PAKDD
2005
ACM

An Incremental Data Stream Clustering Algorithm Based on Dense Units Detection

10 years 9 months ago
An Incremental Data Stream Clustering Algorithm Based on Dense Units Detection
Abstract. The data stream model of computation is often used for analyzing huge volumes of continuously arriving data. In this paper, we present a novel algorithm called DUCstream for clustering data streams. Our work is motivated by the needs to develop a single-pass algorithm that is capable of detecting evolving clusters, and yet requires little memory and computation time. To that end, we propose an incremental clustering method based on dense units detection. Evolving clusters are identified on the basis of the dense units, which contain relatively large number of points. For efficiency reasons, a bitwise dense unit representation is introduced. Our experimental results demonstrate DUCstream’s efficiency and efficacy.
Jing Gao, Jianzhong Li, Zhaogong Zhang, Pang-Ning
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where PAKDD
Authors Jing Gao, Jianzhong Li, Zhaogong Zhang, Pang-Ning Tan
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