Exploring different functions for heuristics, discretization, and rule quality evaluation in ant-miner

Authors:
Khalid M. Salama;Fernando E. B. Otero
Affiliations:
School of Computing, University of Kent, Canterbury, UK;School of Computing, University of Kent, Canterbury, UK
Venue:
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
Year:
2012

Citing 3
Cited 0

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Editorial survey: swarm intelligence for data mining

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Data mining with an ant colony optimization algorithm

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Abstract

Data mining is a process that supports knowledge discovery by finding hidden patterns, associations and constructing analytical models from databases. Classification is one of the widely studied data mining tasks in which the aim is to discover, from labelled cases, a model that can be used to predict the class of unlabelled cases. Ant-Miner, proposed by Parpinelli et al. [3], is the first ACO algorithm for discovering classification rules. Ant-Miner has been shown to be competitive with well-known classification algorithms, in terms of producing comprehensible model with high predictive accuracy. Therefore, there has been an increasing interest in improving the Ant-Miner algorithm [1].