Abstract
In data mining the task of extracting classification rules from large data is an important task and is gaining considerable attention. This article presents a novel ant miner for classification rule mining. The ant miner is inspired by researches on the behaviour of real ant colonies, simulated annealing, and some data mining concepts as well as principles. This paper presents a Pittsburgh style approach for single objective classification rule mining. The algorithm is tested on a few benchmark datasets drawn from UCI repository. The experimental outcomes confirm that ant miner-HPB (Hybrid Pittsburgh Style Classification) is significantly better than ant-miner-PB (Pittsburgh Style Classification).
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