Data Mining with an Ant Colony Optimization Algorithm (2002)
| Venue: | IEEE Transactions on Evolutionary Computation |
| Citations: | 50 - 8 self |
BibTeX
@ARTICLE{Parpinelli02datamining,
author = {Rafael S. Parpinelli and Heitor S. Lopes and Alex A. Freitas},
title = {Data Mining with an Ant Colony Optimization Algorithm},
journal = {IEEE Transactions on Evolutionary Computation},
year = {2002},
volume = {6},
pages = {321--332}
}
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Abstract
Abstract – This work 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 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. Index Terms – Ant Colony Optimization, data mining, knowledge discovery, classification. I.
