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Fuzzy Rules Mining from Decision Tree for Stock Market Prediction

In this paper, data mining technique has been used to uncover hidden patterns and predict future trends and behaviors in financial markets. In data mining, new version of fuzzy decision tree (FDT) has been constructed for extraction of weighted fuzzy production rules (WFPR) by applying proposed method of fuzzy reasoning. Many existing fuzzy reasoning methods are based on Zadeh’s Compositional Rule of Inference (CRI), which requires setting up a fuzzy relation between the antecedent and the consequent part. There are some other fuzzy reasoning methods which do not use Zadeh’s CRI. Among them, the similarity-based fuzzy reasoning methods, which make use of the degree of similarity between a given fact and the antecedent of the rule to draw conclusion are well known. In proposed similarity-based fuzzy reasoning method weight parameter can be assign to each proposition in the antecedent of a fuzzy production rule (FPR) and assign certainty factor (CF) to each rule. Certainty factors have been calculated by using some important variables like effect of other companies, effect of other local stock market, effect of overall world situation, effect of political situation in stock market. The experimental results show that proposed FDT can generate relatively comprehensive tree without much computation effort and can generate WFP rules with high learning accuracy. These generated WFPRs can be used for stock market prediction.