Single and multi-objective ant programming for mining interesting rare association rules. (English) Zbl 1331.68192

Summary: Extracting frequent and reliable rules has been the main interest of the association task of data mining. However, the discovery or infrequent or rare rules is attracting a lot of interest in many domains, such as banking frauds, biomedical data and network intrusion. Most of existent solutions for discovering reliable rules that rarely appear are based on exhaustive classicalapproaches, which have the drawback of becoming infeasible when dealing with high complex data sets, and which do not take into account any measure of the interestingness of the rules mined. This paper explores the application of ant programming, a bio-inspired technique for finding computer programs, to the discovery of rare association rules. To this end, it proposes two algorithms: a first one which evaluates individuals generated from a single-objective point of view, and a second one which considers simultaneously several objectives to evaluate individuals’ fitness. Both of them show their ability to find a high reliable and interesting set of rare rules for the data miner in a short period of time, lacking the drawbacks of exhaustive algorithms.


68T05 Learning and adaptive systems in artificial intelligence
90C29 Multi-objective and goal programming
90C59 Approximation methods and heuristics in mathematical programming
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