Action rules introduced in [3] and investigated further in [4] assume that attributes in a database are divided into two groups: stable and flexible. In general, an action rule can be constructed from two rules extracted earlier from the same database. Furthermore, we assume that these two rules describe two different decision classes and that our goal is to re-classify some objects from one of these decision classes to the other one. Flexible attributes provide a tool for making hints to a business user what changes within some values of flexible attributes are needed for a given object to re-classify this object to another decision class. In [3], we suggested what changes are needed to classification attributes listed in both rules but we did not consider situations when such an attribute is listed only in one of these rules. Also, neither in [3] nor [4] we provide a way to compute support and confidence of action rules. ! In this paper, we show how system DEAR is discovering extended action rules which give better strategies for re-classifying objects than strategies provided by action rules. Also, the confidence of extended action rules is much higher than confidence of corresponding action rules. System DEAR, implemented in KDD Laboratory at UNC-Charlotte, requires Windows 95 or higher. It does not discretize numerical attributes which means some discretization algorithm has to applied before DEAR is used.
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