Investigating the Impact of Bursty Traffic on Hoeffding Tree Algorithm in Stream Mining over Internet

Abstract

Steam data are continuous and ubiquitous in nature which can be found in many Web applications operating on Internet. Some instances of stream data are web logs, online users’ click-streams, online media streaming and Web transaction records. Stream Mining was proposed as a relatively new data analytic solution for handling such streams. It has been widely acclaimed of its usefulness in real-time decision-support applications, for example web recommenders. Hoeffding Tree Algorithm (HTA) is one of the popular choices for implementing Very-Fast-Decision-Tree in stream mining. The theoretical aspects have been studied extensively by researchers. However, the data streams that fed into HTA are usually assumed at a constant rate in the literature. HTA has yet been tested under bursty traffic such as Internet environment. This paper sheds some light into the impact of bursty traffic on the performance of HTA in stream mining.