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Discovering Frequent High Average Utility Itemset Without Transaction Insertion

  • Pramod Singh NegiEmail author
  • Samar Wazir
  • Md. Tabrez Nafis
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)

Abstract

Data mining is a technique through which we can find interesting data and sequence from the available wide range of data source. Incremental high-average utility pattern mining (IHAUPM) algorithm is represented to manage the incremental database with transaction insertion. IHAUPM algorithm basically follows the comparison to the original database and newly inserted database itemset if itemset has High Average Utility Upper Bound Itemset (HAUUBI) in the initial database as well as new transaction database then the item always frequent. Second situation itemset has non-High Average Utility Upper Bound Itemset (non-HAUUBI) in the initial database as well as new transaction database then the item always not frequent. Otherwise, the itemset is recurring or not is identified by the given information. This new algorithm is represented in this paper to generate expected high utility frequent item set to form a new transaction database; this algorithm is much faster than the existing algorithm.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Pramod Singh Negi
    • 1
    Email author
  • Samar Wazir
    • 1
  • Md. Tabrez Nafis
    • 1
  1. 1.Department of Computer Science and EngineeringJamia HamdardNew DelhiIndia

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