Mining Emerging High Utility Itemsets over Streaming Database

Acquah Hackman, Yu Huang, Philip S. Yu, Vincent S. Tseng*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 15th International Conference, ADMA 2019, Proceedings
EditorsJianxin Li, Sen Wang, Shaowen Qin, Xue Li, Shuliang Wang
PublisherSpringer
Pages3-16
Number of pages14
ISBN (Print)9783030352301
DOIs
StatePublished - 19 Nov 2019
Event15th International Conference on Advanced Data Mining and Applications, ADMA 2019 - Dalian, China
Duration: 21 Nov 201923 Nov 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11888 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Advanced Data Mining and Applications, ADMA 2019
CountryChina
CityDalian
Period21/11/1923/11/19

Keywords

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

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  • Cite this

    Hackman, A., Huang, Y., Yu, P. S., & Tseng, V. S. (2019). Mining Emerging High Utility Itemsets over Streaming Database. In J. Li, S. Wang, S. Qin, X. Li, & S. Wang (Eds.), Advanced Data Mining and Applications - 15th International Conference, ADMA 2019, Proceedings (pp. 3-16). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11888 LNAI). Springer. https://doi.org/10.1007/978-3-030-35231-8_1