Fuzzy Decision Tree for Data Mining of Time Series Stock Market Databases

If the given fact for an antecedent in a fuzzy production rule (FPR) does not match exactly with the antecedent of the rule, the consequent can still be drawn by technique such as 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 similaritybased 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 this paper, new Fuzzy Decision Tree (FDT) has been constructed by using weighted fuzzy production rules (WFPR). In WFPR, assign a weight parameter 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 (e.g. effect of other companies, effect of other stock exchanges, effect of overall world situation, effect of political situation etc) in dynamic stock market. Finally, our proposed approach will be able to predict stock share indices, and improve computational efficiency of data mining approaches .