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 mining
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