Decision Tree Regression Algorithm for Mining High-Speed Data Streams

Abstract

In recent years, advances in hardware technology have facilitated new ways of collecting high speed data continuously. Marvelous and possibly Infinite volumes of data streams are often generated by communication networks, remote sensors and other dynamic environments. Mining these data streams raises new problems in terms of how to mine continuous high speed data items that you can only have one look at. In this paper, we propose algorithm output is a solution for mining high speed data streams. The decision tree algorithm builds regression models in the form of a tree structure. The tree breaks down into smaller subsets while at the same time its associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. A decision node has two or more branches each representing values for the attribute tested. Leaf node represents a decision on the numerical target. The topmost decision node in a tree which corresponds to the best predictor called root node. Decision trees can handle both unconditional and numerical data.

Keywords: Decision Tree Regression, Mining Decision Tree Regression, Regression Tree

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