Data mining with an ant colony optimization algorithm

被引：580

作者：

Parpinelli, RS ^{[1
]}

Lopes, HS

Freitas, AA

机构：

[1] Ctr Fed Educ Tecnol Parana, Coordenacao Posgrad Engn Elect & Informat Ind, BR-80230901 Curitiba, Parana, Brazil

[2] Pontificia Univ Catolica Parana, Ctr Ciencias Exatas & Tecnol, Programa Posgrad Informat Aplicada, BR-80215901 Curitiba, Parana, Brazil

来源：

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION | 2002年 / 6卷 / 04期

关键词：

ant colony optimization; classification; data mining; knowledge discovery;

D O I：

10.1109/TEVC.2002.802452

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：321 / 332

页数：12

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