HUE-Stream: evolution-based clustering technique for heterogeneous data streams with uncertainty

Authors:
Wicha Meesuksabai;Thanapat Kangkachit;Kitsana Waiyamai
Affiliations:
Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand;Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand;Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand
Venue:
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Year:
2011

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Abstract

Evolution-based stream clustering method supports the monitoring and the change detection of clustering structures. E-Stream is an evolution-based stream clustering method that supports different types of clustering structure evolution which are appearance, disappearance, self-evolution, merge and split. This paper presents HUE-Stream which extends E-Stream in order to support uncertainty in heterogeneous data. A distance function, cluster representation and histogram management are introduced for the different types of clustering structure evolution. We evaluate effectiveness of HUE-Stream on real-world dataset KDDCup 1999 Network Intruision Detection. Experimental results show that HUE-Stream gives better cluster quality compared with UMicro.