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A Novel Algorithm for Utility-Frequent Itemset Mining in Market Basket Analysis

  • M. A. JabbarEmail author
  • B. L. Deekshatulu
  • Priti Chandra
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 424)

Abstract

Data mining has made a significant impact on business and knowledge management in recent years. Data mining also known as KDD is a powerful new technology having great potential to analyze useful information stored in large data bases. Association rule mining is an active research area, used to find association and/or correlation among frequent item sets. Association rule mining discover frequent item sets with out considering total cost, quantity and number of items in a transaction. Utility based frequent item set mining is a new research area, which provide importance to sale quantity and price among items in a transaction. In this paper, we have proposed a novel approach for utility frequent item set mining. Our method mines novel frequent item sets by giving importance to items quantity, significance weightage, utility and user defined support. Our approach can be used to provide valuable recommendation to the enterprise to improve business utility.

Keywords

Data mining Utility mining Significance weight age Frequent item sets 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • M. A. Jabbar
    • 1
    Email author
  • B. L. Deekshatulu
    • 2
  • Priti Chandra
    • 3
  1. 1.Muffakham Jah College of Engineering and TechnologyHyderabadIndia
  2. 2.IDRBTRBIHyderabadIndia
  3. 3.ASLDRDOHyderabadIndia

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