Discovering cost-effective action rules

Kalanat,, N; Shamsinejad, P; Saraee, MH

Abstract

Mining informative patterns from databases is the historical task of data mining. But now, mining actionable patterns is becoming the new duty of data mining. Most of machine learning and data mining algorithms only focus on finding patterns and usually don't take any step for suggesting actions and users will be responsible for it. Therefore users will be faced with many patterns that they are confused about how and what to do with them. So that extracting actionable knowledge from database, to offer actions that lead to an increase in profit is very critical.
Up to now few works have been done in this field and they usually suffer from drawbacks such as incomprehensibility to the user, neglecting cost, not providing rule generality. Here we attempt to present a method to resolving these issues. In this paper CEARDM method is proposed to discovering cost-effective action rules from data. These rules offer some cost-effective changes to transferring low profitable instances to higher profitable ones.

• Presentation Conference Type: Other
• Conference Name: 4th IEEE International Conference on Computer Science and Information Technology (IEEE ICCSIT 2011),
• Start Date: Jun 10, 2011
• End Date: Jun 12, 2011
• Publication Date: Jan 1, 2011
• Deposit Date: Oct 27, 2011
• Publicly Available Date: Apr 5, 2016
• Additional Information: Event Type : Conference