Survey on Gaussian Estimation Based Decision Trees for Data Streams Mining

In the literature, Hoeffding tree algorithm was projected, for mining data streams decision trees became one of the most popular tools. Determine the best attribute to split the considered node is the key point of constructing the decision tree. Existing system presented the numerous methods to solve this problem. But they are either time consuming such as, in the MacDiarmid tree algorithm or justified wrongly by mathematically such as, in the Hoeffding tree algorithm. The selection of best attribute in the considered node with the help of finite data sample is similar as it would be in the case of the entire data stream with the high probability set by the user is make sure by the this method. In this paper we are presenting some efficient research approaches suggested by numerous scholars.