Generating Optimized Decision Tree Based on Discrete Wavelet Transform

Increasing growth of functionality in current IT trends proved the decision making operations through mass data mining techniques. There is still a requirement for further efficiency and optimization. The problem of constructing the optimization decision tree is now an active research area. Generating an efficient and optimized decision tree with multi-attribute data source is considered as one of the shortcomings. This paper emphasizes to propose a multivariate statistical method Discrete Wavelet Transform on multi-attribute data for reducing dimensionality and to transform traditional decision tree algorithm to form a new algorithmic model. The experimental results described that this method can not only optimizes the structure of the decision tree, but also improves the problems existing in pruning and to mine the better rule set without effecting the purpose of prediction accuracy altogether.