Title :
Data mining with an ant colony optimization algorithm
Author :
Parpinelli, Rafael S. ; Lopes, Heitor S. ; Freitas, Alex A.
Author_Institution :
Coordenacao de Pos-Graduacao em Engenharia Eletrica e Informatica Ind., Centro Fed. de Educacao Tecnologica do Parana, Curitiba, Brazil
fDate :
8/1/2002 12:00:00 AM
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
Keywords :
data mining; knowledge based systems; optimisation; pattern classification; Ant-Miner; CN2; ant colony optimization algorithm; classification rule extraction; data mining algorithm; knowledge discovery; predictive accuracy; public domain data sets; real ant colonies; rule lists; Accuracy; Ant colony optimization; Classification algorithms; Clustering algorithms; Data mining; Databases; Decision making; Humans; Machine learning; Statistics;
Journal_Title :
Evolutionary Computation, IEEE Transactions on
DOI :
10.1109/TEVC.2002.802452
