E-Stream: Evolution-Based Technique for Stream Clustering

Authors:
Komkrit Udommanetanakit;Thanawin Rakthanmanon;Kitsana Waiyamai
Affiliations:
Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand;Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand;Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand
Venue:
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Year:
2007

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Abstract

Data streams have recently attracted attention for their applicability to numerous domains including credit fraud detection, network intrusion detection, and click streams. Stream clustering is a technique that performs cluster analysis of data streams that is able to monitor the results in real time. A data stream is continuously generated sequences of data for which the characteristics of the data evolve over time. A good stream clustering algorithm should recognize such evolution and yield a cluster model that conforms to the current data. In this paper, we propose a new technique for stream clustering which supports five evolutions that are appearance, disappearance, self-evolution, merge and split.