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Association rules are a class of important regularities in databases. They are found to be very useful in practical applications. However, association rule mining algorithms tend to produce a huge number of rules, most of which are of no interest to the user. Due to the large number of rules, it is very difficult for the user to analyze them manually to identify those truly interesting ones. This article presents a new approach to assist the user in finding interesting rules (in particular, unexpected rules) from a set of discovered association rules. This technique is characterized by analyzing the discovered association rules using the user's existing knowledge about the domain and then ranking the discovered rules according to various interestingness criteria, e.g., conformity and various types of unexpectedness. This technique has been implemented and successfully used in a number of applications.
subjective interestingness, association rules, interestingness analysis in data mining.

Y. Ma, W. Hsu, B. Liu and S. Chen, "Analyzing the Subjective Interestingness of Association Rules," in IEEE Intelligent Systems, vol. 15, no. , pp. 47-55, 2000.
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