Density-Based Projected Clustering of Data Streams

In this paper, we have proposed, developed and experimentally validated our novel subspace data stream clustering, termed PreDeConStream. The technique is based on the two phase mode of mining streaming data, in which the first phase represents the process of the online maintenance of a data structure, that is then passed to an offline phase of generating the final clustering model. The technique works on incrementally updating the output of the online phase stored in a micro-cluster structure, taking into consideration those micro-clusters that are fading out over time, speeding up the process of assigning new data points to existing clusters. We have used a density based projected clustering model in developing PreDeConStream. With many important applications that can benefit from such technique, we have proved experimentally the superiority of the proposed methods over state-of-the-art techniques.

Authors: Hassani M., Spaus P., Mohamed Medhat Gaber, Seidl T.
Published in: The 6th International conference on Scalable Uncertainty Management (SUM 2012), Marburg, Germany. LNAI 7520
Publisher: Springer
Sprache: EN
Jahr: 2012
Seiten: 311-324
ISBN: 978-3-642-33362-0
ISSN: 0302-9743
Konferenz: SUM
URL:SUM 2012
Typ: Tagungsbeiträge
Forschungsgebiet: Fast Access to Complex Data