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Decision Tree Classification Implementation with Fuzzy Logic


Renuka D. Suryawanshi, D. M. Thakore


Vol. 12  No. 10  pp. 93-97


Data mining is having an aim to analyze the observation datasets to find relationship and to present the data in ways that are both understandable and usable. This paper basically focuses on the classification technique of datamining to identify the class of an attribute with an ID3 (classical decision tree approach) and then to add fuzzification to improve the result of ID3.It also contains design and implementation of this combined approach with chosen datasets. Id3 results are based on information gain theory and Entropy values of each attribute. Fuzzy ID3 results are based on information gain of fuzzy dataset and fuzzy entropy. Classification results are presented as decision tree which incorporates the result of Id3 & FID3.


ID3(Iterative Dichotomizer 3),FID3(Fuzzy iterative dichotomizer3),CLS(ConceptLearningSystem),IG(Information Gain)