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A Study and Evaluation of Hoeffding Tree for Stream Data Mining

Mattal Sneha Abraham, R. Bhargavi


Nowadays data is increasing day by day on a large scale. Data streaming is the process of extracting knowledge from continuous data leading to various hardship. The goal is to predict the value of new instances in the data stream based on the previous instances in the data stream. This paper describes and evaluates Hoeffding tree. VFDT starts with a single leaf and allows collection of training examples from a data stream. VFDT’s properties is been studied over here and with demonstration it is been tested. VFDT is applied over continuous stream of Web data in Washington Campus.

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