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Mining Emerging High Utility Itemsets over Streaming Database

  • Acquah Hackman
  • Yu Huang
  • Philip S. Yu
  • Vincent S. TsengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)

Abstract

HUIM (High Utility Itemset Mining) is a classical data mining problem that has gained much attention in the research community with a wide range of applications. The goal of HUIM is to identify all itemsets whose utility satisfies a user-defined threshold. In this paper, we address a new and interesting direction of high utility itemsets mining, which is mining temporal emerging high utility itemsets from data streams. The temporal emerging high utility itemsets are those that are not high utility in the current time window of the data stream but have high potential to become a high utility in the subsequent time windows. Discovery of temporal emerging high utility itemsets is an important process for mining interesting itemsets that yield high profits from streaming databases, which has many applications such as proactive decision making by domain experts, building powerful classifiers, market basket analysis, catalogue design, among others. We propose a novel method, named EFTemHUI (Efficient Framework for Temporal Emerging HUI mining), to identify Emerging High Utility Itemsets better. To improve the efficiency of the mining process, we devise a new mechanism to evaluate the high utility itemsets that will emerge, which has the ability to capture and store the information about potential high utility itemsets. Through extensive experimentation using three datasets, we proved that the proposed method yields excellent accuracy and low errors in the prediction of emerging patterns for the next window.

Keywords

High utility itemset Utility pattern mining Emerging patterns Data stream Data mining 

Notes

Acknowledgements

This research was partially supported by Ministry of Science and Technology, Taiwan, under grant no. 108-2218-E-009-051.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Acquah Hackman
    • 1
  • Yu Huang
    • 2
  • Philip S. Yu
    • 3
  • Vincent S. Tseng
    • 2
    Email author
  1. 1.Department of EECS-IGPNational Chiao Tung UniversityHsinchuTaiwan, ROC
  2. 2.Department of Computer ScienceNational Chiao Tung UniversityHsinchuTaiwan, ROC
  3. 3.Department of Computer ScienceUniversity of Illinois at ChicagoChicagoUSA

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