C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Identifying non-actionable association rules
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Action-Rules: How to Increase Profit of a Company
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Distance Induction in First Order Logic
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Controlled Redundancy in Incremental Rule Learning
ECML '93 Proceedings of the European Conference on Machine Learning
Mining Optimal Actions for Profitable CRM
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Postprocessing Decision Trees to Extract Actionable Knowledge
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A decision-theoretic approach to data mining
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Environmental Modelling & Software
Computers and Electronics in Agriculture
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Rule induction has attracted a great deal of attention in Machine Learning and Data Mining. However, generating rules is not an end in itself because their applicability is not so straightforward. Indeed, the user is often overwhelmed when faced with a large number of rules. In this paper, we propose an approach to lighten this burden when the user wishes to exploit such rules to decide which actions to do given an unsatisfactory situation. The method consists in comparing a situation to a set of classification rules. This is achieved using a suitable distance thus allowing to suggest action recommendations with minimal changes to improve that situation. We propose the algorithm Dakar for learning action recommendations and we present an application to an environmental protection issue. Our experiment shows the usefulness of our contribution in decision-making but also raises concerns about the impact of the redundancy of a set of rules in learning action recommendations of quality.