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首页> 外文期刊>Journal of Emerging Technologies in Web Intelligence >Stream Mining Dynamic Data by Using iOVFDT
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Stream Mining Dynamic Data by Using iOVFDT

机译：使用iOVFDT进行流数据挖掘动态数据

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—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.
机译：-动态数据是指连续产生的数据，其容量可能等于无穷大。它们可以在许多日常应用中找到，例如电子商务，安全监视和活动监视。这样的数据需要新一代的挖掘算法，称为流挖掘，该算法能够挖掘动态数据而无需先存档它们。本文研究了一种称为iOVFDT的重要流挖掘方法在动态数据环境下的有效性，该方法代表增量优化的超快速决策树。在实验中测试了具有不同特征的六个动态数据方案。每种类型的动态数据都代表一个决策问题，该决策问题需要有效的分类机制（例如决策三通）来将新案件快速准确地分类为已定义的组。 iOVFDT与其他流行的流挖掘算法进行了比较，并显示了其卓越的性能。

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