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Discovering Fuzzy Association Rules with Interest and Conviction Measures

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3684))

Abstract

Association rule mining forms an important research area in the field of data mining. The theory of fuzzy sets can be used over relational databases to discover useful, meaningful patterns. In this paper, we propose an algorithm to mine fuzzy association rules over relational databases using Interest and Conviction measures. In the present work, we introduce fuzzy interest and fuzzy conviction measures and eliminate the rules, which have negative correlation. The experiments are conducted on an insurance database using our approach. The presented approach is very useful and efficient when there are more infrequent itemsets in a database.

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Author information

Authors and Affiliations

  1. Institute for Development and Research in Banking Technology, Masab Tank, Hyderabad, 500057, Andhra Pradesh, India

    K. Sai Krishna & P. Radha Krishna

  2. XLRI- Jamshedpur, C.H.Area (E), Jamshedpur, 831001, Jharkhand, India

    Supriya Kumar De

Authors
  1. K. Sai Krishna
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  2. P. Radha Krishna
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  3. Supriya Kumar De
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Editor information

Editors and Affiliations

  1. School of Business, La Trobe University, 3086, Melbourne, Victoria, Australia

    Rajiv Khosla

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, Moulsecoomb, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2005 Springer-Verlag Berlin Heidelberg

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Krishna, K.S., Krishna, P.R., De, S.K. (2005). Discovering Fuzzy Association Rules with Interest and Conviction Measures. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11554028_15

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  • DOI: https://doi.org/10.1007/11554028_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28897-8

  • Online ISBN: 978-3-540-31997-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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