Data mining with an ant colony optimization algorithm

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...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2002-08, Vol.6 (4), p.321-332
Main Authors: Parpinelli, R.S., Lopes, H.S., Freitas, A.A.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Ant colony optimization
Applied sciences
Artificial intelligence
Classification
Classification algorithms
Clustering algorithms
Colonies
Computer science
control theory
systems
Data mining
Decision making
Evolutionary algorithms
Exact sciences and technology
Humans
Learning and adaptive systems
Lists
Machine learning
Operational research and scientific management
Operational research. Management science
Optimization
Optimization. Search problems
Statistics
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Description
Summary: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.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2002.802452