Density-Based projected clustering of data streams

Authors:
Marwan Hassani;Pascal Spaus;Mohamed Medhat Gaber;Thomas Seidl
Affiliations:
Data Management and Data Exploration Group, RWTH Aachen University, Germany;Data Management and Data Exploration Group, RWTH Aachen University, Germany;School of Computing, University of Portsmouth, UK;Data Management and Data Exploration Group, RWTH Aachen University, Germany
Venue:
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Year:
2012

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Abstract

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. A density based projected clustering model in developing PreDeConStream was used. 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.