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This paper describes how we applied a fuzzy technique to a data-mining task involving a large database that was provided by an international bank with offices in Hong Kong. The database contains the demographic data of over 320,000 customers and their banking transactions, which were collected over a six-month period. By mining the database, the bank would like to be able to discover interesting patterns in the data. The bank expected that the hidden patterns would reveal different characteristics about different customers so that they could better serve and retain them. To help the bank achieve its goal, we developed a fuzzy technique, called Fuzzy Association Rule Mining II (FARM II), which can mine fuzzy association rules. FARM II is able to handle both relational and transactional data. It can also handle fuzzy data. The former type of data allows FARM II to discover multidimensional association rules, whereas the latter data allows some of the patterns to be more easily revealed and expressed. To effectively uncover the hidden associations in the bank-account database, FARM II performs several steps. First, it combines the relational and transactional data together by performing data transformations. Second, it identifies fuzzy attributes and performs fuzzification so that linguistic terms can be used to represent the uncovered patterns. Third, it makes use of an efficient rule-search process that is guided by an objective interestingness measure. This measure is defined in terms of fuzzy confidence and support measures, which reflect the differences in the actual and the expected degrees to which a customer is characterized by different linguistic terms. These steps are described in detail in this paper.With FARM II, fuzzy association rules were obtained that were judged by experts from the bank to be very useful. In particular, they discovered that they had identified some interesting characteristics about the customers