In this thesis, we present a practical framework for mining quantitative association rules and give recommendations regarding presentation of the discovered rules. A system for mining and working with quantitative association rules was designed. We suggest a general system architecture that covers a range of practical applications, spreads logically separate functionality between several independent modules, and scales well with the number of clients served by the system. Further, we motivate a choice of a suitable mining technique and present an alternative mechanism for generating rules from trend rule candidates. A workflow allowing users without previous background in data mining to make effective use of mined rules to support everyday work is described. Two models for assessing interestingness of rules are suggested. Impact is an interestingness measure based on the difference between the observed average and the expected average as well as on the number of data records behind the rule. Hotness is a user-specific interestingness value combining statistical significance of the rule with collective intelligence of all users and personal preferences of the current user. The segment-to-item technique for visualizing multiple rules in a compact and accessible format was developed, as well as the metasegment-to-item approach that is a further generalization of this idea. In addition, ways of using data visualization to facilitate analysis of individual rules are described. A working system based on these ideas was implemented and integrated into an industrial software product proving that the developed techniques and designs are feasible in a practical application.