Mining fuzzy association rules with 2-tuple linguistic terms in stock market data by using genetic algorithm
Hadi Lafzi Ghazi, Mohammad Saniee Abadeh · 2012
An evolutionary approach for finding fuzzy association rule with 2-tuple linguistic representation model is presented in this work. We propose a method based on multi objective genetic algorithm for identifying fuzzy association rules without specifying minimum support and minimum confidence. In fact our algorithm extracts both association rules and membership function in one step. Also we use Iterative Rule Learning (IRL) process to try to cover those instances that were still uncovered. To evaluate the proposed algorithm we use the stock price dataset and compare our results with the fuzzy mining approach which uses uniform fuzzy partitioning to extract fuzzy association rule. Obtained results show that our technique outperforms the uniform fuzzy partitioning method.