Author:

Keywords:

data mining, causal inference, action rule mining, machine learning, Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Action mining, causal networks, causal rules, ACTION RULES DISCOVERY, SYSTEM, 0801 Artificial Intelligence and Image Processing, 0804 Data Format, 1702 Cognitive Sciences, Artificial Intelligence & Image Processing, 4602 Artificial intelligence, 4605 Data management and data science, 4611 Machine learning

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 this effect. 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.