期刊名称：Journal of Emerging Technologies in Web Intelligence
印刷版ISSN：1798-0461
出版年度：2013
卷号：5
期号：1
页码：78-86
DOI：10.4304/jetwi.5.1.78-86
语种：English
出版社：Academy Publisher
摘要：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.
