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Action rules discovery: system DEAR2 , method and experiments

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Subjective measures, used to model interestingness of rules, are user-driven, domain-dependent, and include unexpectedness, novelty and actionability ( Adomavicius and Tuzhilin 1997 , Liu et al . 1997 , Silberschatz and Tuzhilin 1995 ). Liu et al . (1997) define a rule as actionable, if a user can do an action to his/her advantage based on that rule. Their notion of actionability is too vague and leaves the door open to a number of different interpretations. Raś and Wieczorkowska (2000) assume that actionability has to be expressed in terms of attributes that are present in the database. They have introduced a new class of rules (called action rules) that are constructed from certain pairs of association rules extracted from that database. A conceptually similar definition of an action rule was proposed independently by Geffner and Wainer (1998) . Action rules have been investigated further in Raś and Gupta (2002) , Raś and Tsay (2003) , Raś et al . (2005) and Tzacheva and Raś (2004) . In order to construct action rules, it is required that attributes in a database are divided into two groups: stable and flexible. Flexible attributes are used in a decision rule as a tool for making hints to a user what changes within some of their values are needed to reclassify a group of objects from one decision class into another one. Two strategies for generating action rules are presented. The first one, implemented as system DEAR , generates action rules from certain pairs of association rules. The second one, implemented as system DEAR2 , is based on a tree structure that partitions the set of rules, having the same decision value, into equivalence classes each labelled by values of stable attributes (two rules belong to the same equivalence class, if values of their stable attributes are not conflicting each other). Now, instead of comparing all pairs of rules, only pairs of rules belonging to some of these equivalence classes are compared to construct action rules. This strategy significantly reduces the number of steps needed to generate action rules in comparison to DEAR system.
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Keywords: Actionability; Decision system; Interestingness; Knowledge discovery

Document Type: Research Article

Affiliations: Department of Computer Science University of North Carolina 9201 University City Blvd Charlotte NC 28223 USA

Publication date: January 1, 2005

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