Causality-based cost-effective action mining

Shamsinejadbabki, P, Saraee, MH ORCID: https://orcid.org/0000-0002-3283-1912 and Blockeel, H 2013, 'Causality-based cost-effective action mining' , Intelligent Data Analysis, 17 (6) , pp. 1075-1091.

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Abstract

In many business contexts, the ultimate goal of knowledge discovery is not the knowledge itself, but putting it to use. Models or patterns found by data mining methods often require further post-processing to bring this about. For instance, in churn prediction, data mining may give a model that predicts which customers are likely to end their contract, but companies are not just interested in knowing who is likely to do so, they want to know what they can do to avoid this. The models or patterns have to be transformed into actionable knowledge. Action mining explicitly addresses this. Currently, many action mining methods rely on a predictive model, obtained through data mining, to estimate the effect of certain actions and finally suggest actions with desirable effects. A major problem with this approach is that predictive models do not necessarily reflect a causal relationship between their inputs and outputs. This makes the existing action mining methods less reliable. In this paper, we introduce ICE-CREAM, a novel approach to action mining that explicitly relies on an automatically obtained best estimate of the causal relationships in the data. Experiments confirm that ICE-CREAM performs much better than the current state of the art in action mining.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Intelligent Data Analysis
Publisher: IOS Press
ISSN: 1088-467X
Related URLs:
Depositing User: Prof. Mo Saraee
Date Deposited: 03 Sep 2018 13:40
Last Modified: 24 Nov 2019 08:01
URI: http://usir.salford.ac.uk/id/eprint/48185

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