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Mining and Ranking Association Rules in Support, Confidence, Correlation, and Dissociation Framework

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Proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA) 2015

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 404))

Abstract

Existing methods in association rule mining based on traditional support-confidence framework generates huge number of frequent patterns and association rules often ignoring the dissociation among items. Moreover these procedures are unable to order the rules by comparing them to find which one is better than whom. We have introduced a new algorithm for mining frequent patterns based on support and dissociation and thereafter generating rules based on confidence and correlation. The association rules have been ranked based on a composite index computed from the four measures. The experimental results obtained after implementation of the proposed algorithm justify our approach.

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Notes

  1. 1.

    Togetherness is the ratio of association to association plus dissociation. Togetherness is same as Jaccard Similarity coefficient.

  2. 2.

    ws is defined as \( \Phi {\text{s}}\,{ + }\,\left( {1 -\Phi } \right){\rm j} \) where \( {\varPhi }={{\text{f}_{00} } \mathord{\left/ {\vphantom {{\text{f}_{00} } {\text{N}}}} \right. } {\text{N}}} \) i.e., percentage of null transactions in the database for the rule.

  3. 3.

    If the two variables are independent then ρ equals 0. ρ = +1 signifies positive correlation and \( \uprho = {-}1 \) signifies negative correlation.

  4. 4.

    These datasets can be downloaded from http://wiki.csc.calpoly.edu/datasets/wiki/.

  5. 5.

    In the figures X-Axis represents max dissociation, Primary Y-Axis represents frequent itemsets & Rules and Secondary Y-Axis represents Max and Min Rank Index.

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Authors and Affiliations

  1. Neotia Institute of Technology, Management and Science, Kolkata, India

    Subrata Datta & Subrata Bose

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  1. Subrata Datta
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Correspondence to Subrata Datta .

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Editors and Affiliations

  1. Machine Intelligence Unit, ISI, Kolkata, West Bengal, India

    Swagatam Das

  2. Computer Science and Engineering, National Institute of Technology, Durgapur, West Bengal, India

    Tandra Pal

  3. National Institute of Technology, Durgapur, West Bengal, India

    Samarjit Kar

  4. Deparment of CSE, Anil Neerukonda Ins. of Tech. & Sci., Vishakapatnam, India

    Suresh Chandra Satapathy

  5. Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

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Datta, S., Bose, S. (2016). Mining and Ranking Association Rules in Support, Confidence, Correlation, and Dissociation Framework. In: Das, S., Pal, T., Kar, S., Satapathy, S., Mandal, J. (eds) Proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA) 2015. Advances in Intelligent Systems and Computing, vol 404. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2695-6_13

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  • DOI: https://doi.org/10.1007/978-81-322-2695-6_13

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