Analysis of Entropy Measures using ID3 Algorithm with Iris Dataset
Data Mining is a traditional approach which is used to predict the productive intelligence from the enormous magnitude of data. Data Mining techniques are incorporated with machine learning approaches. To identify and analyze the large biological data, machine learning techniques are applied. Supervised learning approaches are much useful for classifying the data with labeled attributes. ID3 is the successful classifier for qualitative attributes, but tougher to handle the huge volume of continuous labeled data with time complexity. Entropy is a measure used to find the uncertainty and randomness of an attribute in the data set. Information gain and Gain ratio are the relative measures working under Entropy. The proposed work aimed to build the ID3 decision tree and its measures using sample data sets.