This research presents a "data mining-based fuzzy intelligent system" (DFIS) approach to estimate behavior of stock price. Data mining is used in three stages to reduce the complexity of the whole data space. At first; noise filtering is used in order to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage K-means is used to divide the data into sub-populations in order to decrease effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule based system will be carried out for each cluster by means of genetic algorithm. We use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the following, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DFIS is shown for International Business Machines Corporation (IBM) and compared the outcome with the results of the other methods. Results with MAPE metric and non-parametric test indicate that DFIS provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for stock price forecasting problems
