Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Elements of information theory
Elements of information theory
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
The ant colony optimization meta-heuristic
New ideas in optimization
Ant algorithms for discrete optimization
Artificial Life
Mining Very Large Databases with Parallel Processing
Mining Very Large Databases with Parallel Processing
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Machine Learning
Simplifying decision trees: A survey
The Knowledge Engineering Review
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This work describes an algorithm for data mining called Ant-Miner (Ant Colony-based Data Miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts and principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide evidence that: (a) Ant-Miner is competitive with CN2 with respect to predictive accuracy; and (b) The rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2.