THE BASE STRATEGY OF ID3 ALGORITHM OF DATA MINING USING HAVRDA AND CHARVAT ENTROPY

Abstract

Data mining algorithm is the mechanism that creates a data mining model. To create a model, an algorithm first analyzes a set of data and looks for specific patterns and trends. The algorithm uses the results of this analysis to define the parameters of the mining model. These parameters are then applied across the entire data set to extract actionable patterns and detailed statistics. Decision tree algorithm is used to select the best path to follow in the standard division. This thesis introduces the use of ID3 algorithm of decision tree. Instead of using Shannon Entropy, we use Havrda and Charvat Entropy. By computing information we set particular property from taken data as root of tree, also sub-root by repeating the process continually, to finally build the most optimized tree. This decision tree helps to take the decision for better analysis of data. We have implemented proposed algorithm in “C” programming language.