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An Efficient Approach for Incremental Association Rule Mining through Histogram Matching Technique

Author

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  • Ajay Kumar

    (Department of Computer Science & Engineering, Jaypee University of Engineering & Technology, Guna, India)

  • Shishir Kumar

    (Department of Computer Science & Engineering, Jaypee University of Engineering & Technology, Guna, India)

  • Sakshi Saxena

    (Department of Computer Science & Engineering, Jaypee University of Engineering & Technology, Guna, India)

Abstract

The objective of the work being presented is to propose an approach for obtaining appropriate association rules when the data set is being incrementally updated. During this process raw data is clustered by K-mean Clustering Algorithm and appropriate rules are generated for each cluster. Further, a histogram and probability density function are also generated for each cluster. When Burst data set is coming to the system, initially the histogram and probability density function of this new data set are obtained. The new data set has to be added to the cluster whose histogram and probability density functions are almost similar. The proposed method is evaluated and explained on synthetic data.

Suggested Citation

  • Ajay Kumar & Shishir Kumar & Sakshi Saxena, 2012. "An Efficient Approach for Incremental Association Rule Mining through Histogram Matching Technique," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 2(2), pages 29-42, April.
    • Handle: RePEc:igg:jirr00:v:2:y:2012:i:2:p:29-42
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