SA Tabu Miner: A hybrid heuristic algorithm for rule induction

Abstract

This paper presents a Hybrid Heuristic algorithm for induction of classification rules called SA Tabu Miner (Simulated Annealing and Tabu Search based Data Miner). The proposed procedure is inspired by both research on heuristic optimization algorithms and rule induction data mining concepts and principles. A comparison is made of the performance of SA Tabu Miner with CN2 and C4.5, well-known data mining algorithms for classification, and Ant-Miner, a recently proposed Ant Colony Optimization based algorithm, over public domain data sets. The results provide evidence that: our algorithm is comparable with CN2, C4.5 and Ant-Miner in terms of predictive accuracy; and the rule lists discovered by our algorithm are considerably simpler (smaller) than those discovered by other algorithms.