Robust and cost-effective approach for discovering action rules

Kalanat, N, Shamsinejad, P and Saraee, MH ORCID: 2011, 'Robust and cost-effective approach for discovering action rules' , International Journal of Machine Learning and Computing, 1 (4) , pp. 325-331.

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The main goal of Knowledge Discovery in Databases is to find interesting and usable patterns, meaningful in their domain. Actionable Knowledge Discovery came to existence as a direct respond to the need of finding more usable patterns called actionable patterns. Traditional data mining and algorithms are often confined to deliver frequent patterns and come short for suggesting how to make these patterns actionable. In this scenario the users are expected to act. However, the users are not advised about what to do with delivered patterns in order to make them usable. In this paper, we present an automated approach to focus on not only creating rules but also making the discovered rules actionable. Up to now few works have been reported in this field which lacking incomprehensibility to the user, overlooking the cost and not providing rule generality. Here we attempt to present a method to resolving these issues. In this paper CEARDM method is proposed to discover cost-effective action rules from data. These rules offer some cost-effective changes to transferring low profitable instances to higher profitable ones. We also propose an idea for improving in CEARDM method.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: International Journal of Machine Learning and Computing
Publisher: International Association of Computer Science and Information Technology Press (IACSIT)
ISSN: 2010-3700
Related URLs:
Depositing User: Prof. Mo Saraee
Date Deposited: 27 Apr 2020 08:14
Last Modified: 16 Feb 2022 04:31

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