DeltaDens – Incremental Algorithm for On–Line Density–Based Clustering

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 185)

Abstract

Cluster analysis of data delivered in a stream exhibits some unique properties. They make the clustering more difficult than it happens for the static set of data. This paper introduces a new DeltaDens clustering algorithm that can be used for this purpose. It is a density–based algorithm, capable of finding an unbound number of irregular clusters. The algorithm’s per–iteration processing time linearly depends on the size of its internal buffer. The paper describes the algorithm and delivers some experimental results explaining its performance and accuracy.

Keywords

Density–based Clustering On–line Clustering Data Streams 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Computing SciencePoznan University of TechnologyPoznańPoland

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