Please use this identifier to cite or link to this item:
|Title:||Identifying non-actionable association rules|
|Authors:||Liu, B. |
|Citation:||Liu, B.,Hsu, W.,Ma, Y. (2001). Identifying non-actionable association rules. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : 329-334. ScholarBank@NUS Repository.|
|Abstract:||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 rules and to be assured that the remaining rules are non-redundant and potentially useful for action.|
|Source Title:||Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 16, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.