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International Conference on Swarm, Evolutionary, and Memetic Computing

SEMCCO 2012: Swarm, Evolutionary, and Memetic Computing pp 628–635Cite as

Evolutionary and Immune Algorithms Applied to Association Rule Mining

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7677))

Abstract

Association rule mining is one of the most important data mining tasks. It corresponds to the determination of rules that associate items to other items in a data set, where the items are attributes in transactional databases. Although evolutionary algorithms have been used in this task for some time, there are few applications of immune algorithms to such problem. This paper presents one typical genetic algorithm plus two clonal selection algorithms applied to association rule mining under the perspective of several measures of interest.

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Author information

Authors and Affiliations

  1. Natural Computing Laboratory (LCoN), Mackenzie Presbyterian University, R. Piauí, 130, Consolação, São Paulo, Brazil

    Danilo Souza da Cunha & Leandro Nunes de Castro

Authors
  1. Danilo Souza da Cunha
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  2. Leandro Nunes de Castro
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, 110016, Delhi, India

    Bijaya Ketan Panigrahi

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, 700108, Kolkata, India

    Swagatam Das

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electronics and Telecom. Engineering, Institute of Technical Education & Research, Siksha ’O’ Anusandhan University, 751030, Bhubaneswar, Odisha, India

    Pradipta Kumar Nanda

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© 2012 Springer-Verlag Berlin Heidelberg

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da Cunha, D.S., de Castro, L.N. (2012). Evolutionary and Immune Algorithms Applied to Association Rule Mining. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_73

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  • DOI: https://doi.org/10.1007/978-3-642-35380-2_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35379-6

  • Online ISBN: 978-3-642-35380-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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