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Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012) pp 193–203Cite as

An Efficient Approach on Rare Association Rule Mining

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 201)

Abstract

:Traditional association mining techniques are based on support-confidence framework, which enable us to generate frequent rules based on frequent itemsets identified on a market basket dataset with reference to two user defined threshold minsup and minconf. However, the infrequent itemsets referred here as rare itemsets ignored by those techniques often carry useful information in certain real life applications. This paper presents an effective method to generate frequent as well as rare itemsets and also consequently the rules. The effectiveness of the proposed method is established over several synthetic and real life datasets. To address the limitations of support-confidence based frequent and rare itemsets generation technique, a multi-objective rule generation method also has been introduced. The method has been found to perform satisfactory over several real life datasets.

Keywords

  • Support
  • confidence
  • frequent rule
  • rare rule
  • MOGA.

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  • DOI: 10.1007/978-81-322-1038-2_17
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Authors and Affiliations

  1. Tezpur University, Napam, Sonitpur, Assam, 784028, India

    N. Hoque, B. Nath & D. K. Bhattacharyya

Authors
  1. N. Hoque
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  2. B. Nath
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  3. D. K. Bhattacharyya
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Corresponding author

Correspondence to N. Hoque .

Editor information

Editors and Affiliations

  1. South Asian University, Chankya Puri, New Delhi, 110021, India

    Jagdish Chand Bansal

  2. ABV - IIITM, Gwalior, Gwalior, 474015, Madhya Pradesh, India

    Pramod Kumar Singh

  3. , Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Kusum Deep

  4. , Department of Paper Technology, Indian Institute of Technology Roorkee, Saharanpur Campus, Roorkee, India

    Millie Pant

  5. , Department of Computer Science, Liverpool Hope University, Office: FML 412, Liverpool, Liverpool, L16 9JD, United Kingdom

    Atulya K. Nagar

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Hoque, N., Nath, B., Bhattacharyya, D.K. (2013). An Efficient Approach on Rare Association Rule Mining . In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 201. Springer, India. https://doi.org/10.1007/978-81-322-1038-2_17

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  • DOI: https://doi.org/10.1007/978-81-322-1038-2_17

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  • Publisher Name: Springer, India

  • Print ISBN: 978-81-322-1037-5

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