Data mining with an ant colony optimization algorithm

By Rafael Stubs Parpinelli, Heitor Silvério Lopes, Alex Alves Freitas. IEEE Transactions on Evolutionary Computation, 2002

1,072 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/a917107b6f72f2baa8f879a96a9846d482ecf884

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Citations in the corpus

A Tutorial on Multilabel Learning (2015, methodology): “It was an extension of the ant colony-based Ant-Miner algorithm [Parpinelli et al. 2002].”
Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power (2010, background): “01 Ant-Miner+ [36] Number of ants = 3000, Max number of uncovered examples = 10 Min number of samples by rule = 10, Max iterations without converge = 10 Max pheromone = 0.”
KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework (2011, methodology): “Ant-Miner [36] An Ant Colony System based using a heuristic function based in the entropy measure for each attribute-value CORE [42] A coevolutionary method which employs as fitness measure a combination of the true positive rate and the false positive rate HIDER [2, 4] A method which iteratively cr”
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis (2010, methodology): “applied the ACO algorithm in data mining problem [21].”
Chemical-Reaction-Inspired Metaheuristic for Optimization (2010, neutral): “Hybrid algorithms with SA and TS are also possible [20] We will never be satisfied with the existing metaheuristics, such as those mentioned above, even though they enjoy great success in solving many optimization problems, e.g., [21]-[23].”
Ant colony optimization: Introduction and recent trends (2005, neutral): “Data mining Parpinelli, Lopes, and Freitas [80]”
Swarm Intelligence in Optimization (2008, background): “Traveling salesman problem Dorigo, Maniezzo, and Colorni [46, 50, 51] Dorigo and Gambardella [49] Stützle and Hoos [164] Quadratic assignment problem Maniezzo [109] Maniezzo and Colorni [111] Stützle and Hoos [164] Scheduling problems Stützle [162] den Besten, Stützle, and Dorigo [41] Gagné, Price, ”
Classification With Ant Colony Optimization (2007, methodology): “The first application of ACO for the classification task was reported in [27] and [30], where the authors introduce the AntMiner algorithm for the discovery of classification rules.”
Ant-based and swarm-based clustering (2007, methodology): “Note that the table does not include applications of ant-based methods to data-mining tasks different from clustering, which can also be found in the literature (see Parpinelli et al. 2002, 2005; Langham 2005).”
Improving the interpretability of classification rules discovered by an ant colony algorithm (2013, methodology): “AND term n > THEN < class value > at each iteration of the algorithm, where the IF part corresponds to the antecedent of the rule and the THEN part corresponds to the consequent of the rule.”