Association Rule Mining (ARM) is a well recognized and interesting area of research in the field of Data Mining. In ARM, significant amount of research work has been reported. On the contrary, very less work has been reported for Negative Association Rule Mining (NARM). ARM concentrates only on positive rules while NARM explores negative rules. Also, most researchers have worked with discrete transaction dataset. So, in this paper, we propose a technique called Negative and Positive Fuzzy Association Rule Mining (NP-FARM), which mines both negative and positive association rules from a fuzzy transaction dataset. NP-FARM algorithm has been implemented and the experimental results determine the optimal minimum support threshold and optimal minimum confidence threshold for the given dataset. Also, the experimental results demonstrate that, as the size of the dataset increases, a negligible change in execution time is witnessed to mine the growing dataset.