Identifying non-actionable association rules

13 years 3 months ago
Identifying non-actionable association rules
Building predictive models and finding useful rules are two important tasks of data mining. While building predictive models has been well studied, finding useful rules for action still presents a major problem. A main obstacle is that many data mining algorithms often produce too many rules. Existing research has shown that most of the discovered rules are actually redundant or insignificant. Pruning techniques have been developed to remove those spurious and/or insignificant rules. In this paper, we argue that being a significant rule (or a non-redundant rule), however, does not mean that it is a potentially useful rule for action. Many significant rules (unpruned rules) are in fact not actionable. This paper studies this issue and presents an efficient algorithm to identify these non-actionable rules. Experiment results on many real-life datasets show that the number of non-actionable rules is typically quite large. The proposed technique thus enables the user to focus on fewer rul...
Bing Liu, Wynne Hsu, Yiming Ma
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2001
Where KDD
Authors Bing Liu, Wynne Hsu, Yiming Ma
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