Attribute value reduction for gaining simpler rules

Decision rules in if-then form are highly readable and suitable for the situations in which users need to understand the rules intuitively. When we suppose the situation in which someone reads rules, a set of decision rules is desired to satisfy the following three conditions: 1) They can explain most of possible situations as a rule set, 2) The size of a rule set is small and thus memorable, 3) Description of each rule is simple and easily understood. In general, however, it is difficult to achieve both 2) and 3) under the condition 1). In addition to typical reduction of attributes, we consider reduction of attribute domains, the number of possible attribute values in each attribute, aiming at obtaining simpler but more readable rules. It brings a large variety of granularity in data representation. Using previously proposed some criteria on the basis of 1) through 3), we rated rule sets obtained at specified levels of granularity in some real-life datasets. The rating was almost consistent to that by a human inspector in readability.