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Algorithm for generalization of action rules to summaries

100%

Tzacheva A. A.

tom Vol. 39, no 2

457-468

A knowledge discovery system is prone to yielding plenty of patterns, presented in the form of rules. Sifting through to identify useful and interesting patterns is a tedious and time consuming process. An important measure of interestingness is: whether or not the pattern can be used in the decision making process of a business to increase profit. Hence, actionable patterns, such as action rules, are desirable. Action rules may suggest actions to be taken based on the discovered knowledge. In this way contributing to business strategies and scientific research. The large amounts of knowledge in the form of rules presents a challenge of identifying the essence, the most important part, of high usability. We focus on decreasing the space of action rules through generalization. In this paper, we propose an improved method for discovering short descriptions of action rules. The new algorithm produces summaries by maximizing the diversity of rule pairs, and minimizing the cost of the suggested actions.

Tree-based Construction of Low-cost Action Rules

63%

Tzacheva A. A. , Tsay L.-S.

2008

tom Vol. 86, nr 1-2

213-225

A rule is actionable, if a user can do an action to his/her advantage based on that rule. Actionability can be expressed in terms of attributes that are present in a database. Action rules are constructed from certain pairs of classification rules, previously extracted from the same database, each defining a preferable decision class. It is assumed that attributes are divided into two groups: stable and flexible. Flexible attributes provide a tool for making hints to a user to what changes within some values of flexible attributes are needed to re-classify a group of objects, supporting the action rule, from one decision class to another, more desirable, one. Changes of values of some flexible attributes can be more expensive than changes of other values. To investigate such cases, the notion of a cost is introduced and it is assigned by an expert to each such a change. Action rules construction involves both flexible and stable attributes listed in certain pairs of classification rules. The values of stable attributes are used to create action forest. We propose a new strategy which combines the action forest algorithm of extracting action rules and a heuristic strategy for generating reclassification rules of the lowest cost. This new strategy presents an enhancement to both methods.