The goal of data mining is to help market managers find relationships among items from large datasets to increase profits. Among the mining techniques, the Apriori algorithm is the most basic and important for association rule mining. Although a lot of mining approaches have been proposed based on the Apriori algorithm, most of them focus on positive association rules, such as R1: ¡§If milk is bought, then bread is bought¡¨. However, rule R1 may confuses users and makes wrong decision if the negative relation rules are not considered. For example, the rule such as R2: ¡§If milk is not bought, then bread is bought¡¨ may also be found. Then, the rule R2 conflicts with the positive rule R1. So, if two rules such as ¡§If milk is bought, then bread is bought¡¨ and ¡§If milk is not bought, then bread is not bought¡¨ are found at the same time, the rules which is called coherent rule may be more valuable.In this thesis, we thus propose two algorithms for solving this problem. The first proposed algorithm is named Highly Coherent Rule Mining algorithm (HCRM), which takes the properties of propositional logic into consideration and is based on Apriori approach for finding coherent rules. The lower and upper bounds of itemsets are also tightened to remove unnecessary check. Besides, in order to improve the efficiency of the mining process, the second algorithm, namely Projection-based Coherent Mining Algorithm (PCA), based on data projection is proposed for speeding up the execution time. Experiments are conducted on real and simulation datasets to demonstrate the performance of the proposed approaches and the results show that both HCRM and PCA can find more reliable rules and PCA is more efficient.