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Stream Mining Dynamic Data by Using iOVFDT

Yang Hang and Simon Fong
Department of Computer and Information Science, University of Macau, Taipa, Macau SAR
Abstract—Dynamic data is referring to data that are being produced continuously and their volume can potentially amount to infinity. They can be found in many daily applications such as e-commerce, security surveillance and activities monitoring. Such data call for a new generation of mining algorithms, called stream mining that is able to mine dynamic data without the need of archiving them first. This paper1 studies the efficacy of a prominent stream mining method, called iOVFDT that stands for Incrementally Optimized Very Fast Decision Tree, under the environments of dynamic data. Six scenarios of dynamic data which have different characteristics are tested in the experiment. Each type of dynamic data represents a decision-making problem which demands an efficient classification mechanism such as decision tee to quickly and accurately classify a new case into a defined group. iOVFDT is compared with other popular stream mining algorithms, and it shows its superior performance.

Index Terms—classification; stream mining; dynamic data  

Cite: Yang Hang and Simon Fong, "Stream Mining Dynamic Data by Using iOVFDT," Journal of Emerging Technologies in Web Intelligence, Vol. 5, No. 1, pp. 78-86, February 2013. doi:10.4304/jetwi.5.1.78-86
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