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Association Rule Mining via Evolutionary Multi-objective Optimization

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8875))

Abstract

In this paper, we formulated association rule mining as a combinatorial, multi-objective global optimization problem by considering measures such as support, confidence, coverage, comprehensibility, leverage, interestingness, lift and conviction. Here, we developed three evolutionary miners viz., Multi-objective Binary Particle Swarm Optimization based association rule miner (MO-BPSO), a hybridized Multi-objective Binary Firefly Optimization and Threshold Accepting based association rule miner (MO-BFFOTA), hybridized Multi-objective Binary Particle Swarm Optimization and Threshold Accepting based association rule miner (MO-BPSOTA) and applied them on various datasets and conclude that MO-BPSO-TA outperforms all others.

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Author information

Authors and Affiliations

  1. Centre of Excellence in CRM & Analytics, Institute for Development and Research in Banking Technology, Hyderabad, 500 057, A.P, India

    Pradeep Ganghishetti & Ravi Vadlamani

  2. SCIS, University of Hyderabad, Hyderabad, 500046, A.P, India

    Pradeep Ganghishetti

Authors
  1. Pradeep Ganghishetti
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  2. Ravi Vadlamani
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Automation, Indian Institute of Science, 560 012, Bangalore, India

    M. Narasimha Murty

  2. Faculty of Engineering and Information Technology, University of Technology Sydney, 2007, NSW, Australia

    Xiangjian He

  3. Department of Computer and Information Sciences, University of Hyderabad, 500046, Hyderabad, India

    Raghavendra Rao Chillarige

  4. LIP6, University Pierre et Marie Curie, 4 Place Jussieu, 75005, Paris, France

    Paul Weng

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Ganghishetti, P., Vadlamani, R. (2014). Association Rule Mining via Evolutionary Multi-objective Optimization. In: Murty, M.N., He, X., Chillarige, R.R., Weng, P. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2014. Lecture Notes in Computer Science(), vol 8875. Springer, Cham. https://doi.org/10.1007/978-3-319-13365-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-13365-2_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13364-5

  • Online ISBN: 978-3-319-13365-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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