Data Mining with an Ant Colony Optimization Algorithm

Parpinelli, Rafael S. and Lopes, Heitor S. and Freitas, Alex A. (2002) Data Mining with an Ant Colony Optimization Algorithm. IEEE Transactions on Evolutionary Computation, 6 (4). pp. 321-332. ISSN 1089778X. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1109/TEVC.2002.802452

Abstract

The 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

Item Type: Article
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 17:59
Last Modified: 14 Jul 2014 08:42
Resource URI: http://kar.kent.ac.uk/id/eprint/13753 (The current URI for this page, for reference purposes)
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