Data mining with an ant colony optimization algorithm

doi:10.1109/TEVC.2002.802452

Data mining with an ant colony optimization algorithm

2002-08-01

668

OA

AI

R

Rafael Stubs Parpinelli *

H

Heitor Silvério Lopes

A

Alex A. Freitas

DOI：10.1109/TEVC.2002.802452

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Abstract

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This paper proposes 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 as well as 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: 1) Ant-Miner is competitive with CN2 with respect to predictive accuracy and 2) the rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2.

Keywords:

ant colony optimization

classification

data mining

knowledge discovery

Data mining with an ant colony optimization algorithm

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