Abstract
During the last ten years, data mining, also known as knowledge discovery in databases, has established its position as a prominent and important research area. Mining association rules is one of the important research problems in data mining. Many algorithms have been proposed to find association rules in databases with binary attributes. In this paper, we deal with the problem of mining association rules in databases containing both quantitative and categorical attributes. An example of such an association might be ``10% of married people between age 50 and 70 have at least 2 cars''''. We introduce a new definition of quantitative association rules based on fuzzy set theory. Using the fuzzy set concept, the discovered rules are more understandable to a human. Moreover, fuzzy sets handle numerical values better than existing methods because fuzzy sets soften the effect of sharp boundaries. The above example could be rephrased eg. 10% of married old people have several cars. In this paper we present a new algorithm for mining fuzzy quantitative association rules. The algorithm uses new definitions for interesting mea- sures. Experimental results show the efficiency of the algorithm for large databases.
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