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Agent Enriched Distributed Association Rules Mining: A Review

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Agents and Data Mining Interaction (ADMI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7103))

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Abstract

Distributed Data Mining (DDM) is concerned with application of the classical Data Mining (DM) approach in a Distributed Computing (DC) environments so that the available resource including communication networks, computing units and distributed data repositories, human factors etc. can be utilized in a better way and on-line, real-time decision support based distributed applications can be designed. A Mobile Agent (MA) is an autonomous transportable program that can migrate under its own or host control from one node to another in a heterogeneous network. This paper highlights the agent based approach for mining the association rules from the distributed data sources and proposed an another framework called Agent enriched Mining of Strong Association Rules (AeMSAR) from Distributed Data Sources. As agent technology paradigm of the DC has gained lots of research in the recent years, therefore, making an alliance of agent and Association Rules Mining(ARM) will help mining the Association rules in a Distributed environment in a better way.

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Author information

Authors and Affiliations

  1. M.M. Institute of Computer Technology and Business Management, MMU, Mullana, 133203, Haryana, India

    G. S. Bhamra

  2. Department of Computer Science & Engineering, TIET, Thapar University, Patiala, 147004, Punjab, India

    A. K. Verma

  3. Department of Computer Sc. & Engineering, DCR University of Sc. and Tech., Murthal, Haryana, 131039, India

    R. B. Patel

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  1. G. S. Bhamra
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  2. A. K. Verma
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  3. R. B. Patel
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Editor information

Editors and Affiliations

  1. Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, PO Box 123, 2007, Sydney, NSW, Australia

    Longbing Cao

  2. Instituto de Informática, Universidade Federal do Rio Grande do Sul (UFRGS), Caixa Postal 15064, 91.501-970, Porto Alegre, RS, Brazil

    Ana L. C. Bazzan

  3. Electrical and Computer Engineering Department, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Andreas L. Symeonidis

  4. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 39, 14th Liniya, 199178, St. Petersburg, Russia

    Vladimir I. Gorodetsky

  5. Department of Knowledge Engineering, Maastricht University, P.O. Box 616, 6200, Maastricht, MD, The Netherlands

    Gerhard Weiss

  6. Department of Computer Science, University of Illinois at Chicago, 851 S. Morgan Street, Room 1138 SEO, 60607, Chicago, IL, USA

    Philip S. Yu

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Bhamra, G.S., Verma, A.K., Patel, R.B. (2012). Agent Enriched Distributed Association Rules Mining: A Review. In: Cao, L., Bazzan, A.L.C., Symeonidis, A.L., Gorodetsky, V.I., Weiss, G., Yu, P.S. (eds) Agents and Data Mining Interaction. ADMI 2011. Lecture Notes in Computer Science(), vol 7103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27609-5_4

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  • DOI: https://doi.org/10.1007/978-3-642-27609-5_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27608-8

  • Online ISBN: 978-3-642-27609-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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