Mining comprehensible rules from data with an ant colony algorithm

Parpinelli, Rafael S. and Lopes, Heitor S. and Freitas, Alex A. (2003) Mining comprehensible rules from data with an ant colony algorithm. In: Bittencourt, Guilherme and Ramalho, Geber L., eds. Mining Comprehensible Rules from Data with an Ant Colony Algorithm. Lecture Notes in Computer Science, 2507/2002. Springer-Verlag, Berlin pp. 259-269. ISBN 3540001247. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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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.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: data mining, machine learning, ant colony algorithms
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: 17 Jul 2014 08:29
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