Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/414194
Title: Data mining with an ant colony optimization algorithm
Authors: Parpinelli, R S;Lopes, H S;Freitas, a a
subject: Science & Technology
Year: 2008
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
Description: 
URI: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1027744
http://localhost/handle/Hannan/443326
http://localhost/handle/Hannan/414194
Appears in Collections:2002-2008

Files in This Item:
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AL356900.pdf328.71 kBAdobe PDF
Title: Data mining with an ant colony optimization algorithm
Authors: Parpinelli, R S;Lopes, H S;Freitas, a a
subject: Science & Technology
Year: 2008
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
Description: 
URI: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1027744
http://localhost/handle/Hannan/443326
http://localhost/handle/Hannan/414194
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL356900.pdf328.71 kBAdobe PDF
Title: Data mining with an ant colony optimization algorithm
Authors: Parpinelli, R S;Lopes, H S;Freitas, a a
subject: Science & Technology
Year: 2008
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
Description: 
URI: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1027744
http://localhost/handle/Hannan/443326
http://localhost/handle/Hannan/414194
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL356900.pdf328.71 kBAdobe PDF