Effective Decision Tree Algorithm For Reality Mining

This project work deals with reality mining and decision tree. Reality mining is the collection and analysis of data where human social behavior is analyzed through machine-sensed environment, with the goal of identifying predictable patterns of behavior. Classification is the process of finding a model that describe and distinguishes data classes, with the purpose of using model to predict the class of objects whose class label is unknown. A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. ID3 is mathematical algorithm for building the decision tree. It builds the tree from the top down recursive divide-and-conquer manner, with no backtracking. Advantages of ID3 are it build fast and short tree. Disadvantage is data may be over fitted and over classified if a small sample is tested. Only one attribute at a time is tested for making decision. This project work: - To study the drawback of existing decision tree algorithms. To compare the decision tree with R using existing implementation. To apply and study the decision tree with reality minin