Data mining is the process of discovering valid, novel and understandable patterns from data while the discovered patterns can be usable and actionable for business decisions. most of machine learning and data mining techniques only focus on finding frequent patterns and usually do not pay any attention to actionably and usability of mined patterns. Thus data mining is converted to a data-driven trial-and-error process that faces users with many patterns and they will be confused about how and what to do with them. It is because the discovered knowledge by data mining methods is not actionable to satisfy the real world requirements. In order to solve this problem, data mining must be developed towards real world business. For this developing it needs to consider domain factors and constraints in data mining process. Actionable Knowledge Discovery is a paradigm shift from data-driven data mining toward domain-driven data mining that is aimed at discovering actionable knowledge to satisfy real world requirements. Up to now many researches have been done on AKD that have considered some of real world factors and constraints such as changeable or not changeable attributes, cost of change of value's attributes, distributed data, incomplete data. All of these researchesassume that the data is precise, while in most of real world scenarios we never face with quite precise values and always face with a degree of uncertainty. Therefore, accuracy or precision in real world situations is simplifying and idealizing that cause high rate of error in AKD methods. Consequently some useful actionable knowledge strategies can be missed during the search process of these methods and even some non-actionable knowledge strategies may be produced. In this thesis these drawbacks will be overcome by fuzzy set theory. Wepropose the idea of combining fuzzy set theory with actionable knowledge. In this regard and in order to improve the only existent methodon discovering actions (Leaf Node Search Method) one type of fuzzy actionable knowledge named fuzzy action will be introduced and the idea of considering cost of change of attribute values as a function will be proposed. Also a profit function will be presented for predicting the net profit of each fuzzy action. In this thesis, we present an algorithm that suggests fuzzy action in order to decrease the degree to which a certain object (for example a customer) belongs to an undesired status and increase the degree to which it belongs to a desired one while maximizing objective function, namely the expected net profit. The contribution of this thesis is in taking the output from fuzzy decision trees, for producing actionable knowledge through automatic post processing. The effectiveness of our proposed method will be verified on four public data set of UCI in comparison with the leaf-node search method. It will be shown that our method is more efficient than Leaf-Node Search method in a number of suggested actions, total profit and average profit. Keywords: Actionable knowledge, Fuzzy action, Fuzzy decision tree, Post process