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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2008: Artificial Intelligence and Soft Computing – ICAISC 2008 pp 681–692Cite as

Parallel Ant Miner 2

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5097))

Abstract

In this paper, we propose a flexible parallel ant colony algorithm for classification rule discovery in the large databases. We call this algorithm Parallel Ant-Miner2. This model relies on the extension of real behavior of ants and data mining concepts. The artificial ants are firstly generated and separated into several groups. Each group is assigned a class label which is the consequent parts of the rules it should discover. Ants try to discover rules in parallel and then communicate with each other to update the pheromones in different paths. The communication methods help ants not to gather irrelevant terms of the rule. The parallel executions of ants reduce the speed of convergence and consequently make it possible to extract more new high quality rules by exploring all search space. Our experimental results show that the proposed model is more accurate than the other versions of Ant-Miner.

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Author information

Authors and Affiliations

  1. PhD Student of Software Engineering, Isfahan University, Iran

    Omid Roozmand

  2. Assistant Professor of Isfahan University, Iran

    Kamran Zamanifar

Authors
  1. Omid Roozmand
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  2. Kamran Zamanifar
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Editor information

Leszek Rutkowski Ryszard Tadeusiewicz Lotfi A. Zadeh Jacek M. Zurada

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© 2008 Springer-Verlag Berlin Heidelberg

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Roozmand, O., Zamanifar, K. (2008). Parallel Ant Miner 2. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_66

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  • DOI: https://doi.org/10.1007/978-3-540-69731-2_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69572-1

  • Online ISBN: 978-3-540-69731-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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