Mining trending high utility itemsets from temporal transaction databases

Acquah Hackman, Yu Huang, S. Tseng*

*Corresponding author for this work

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

2 Scopus citations

Abstract

In this paper, we address a novel and important topic in the area of HUI mining, named Trending High Utility Itemset (TrendHUI) mining, with the promise of expanding the applications of HUI mining with the power of trend analytics. We introduce formal definitions for TrendHUI mining and highlighted the importance of the TrendHUI output. Moreover, we develop two algorithms, Two-Phase Trending High Utility Itemset (TP-THUI) miner and Two-Phase Trending High Utility Itemset Guided (TP-THUI-Guided) miner. Both are two-phase algorithms that mine a complete set of TrendHUI. TP-THUI-Guided miner utilizes a remainder utility to calculate the temporal trend of a given itemset to reduce the search space effectively, such that the execution efficiency can be enhanced substantially. Through a series of experiments, using three different datasets, the proposed algorithms prove to be excellent for validity and efficiency. To the best of our knowledge, this is the first work addressing the promising topic on Trending High Utility Itemset mining, which is expected to facilitate numerous applications in data mining fields.

Original languageEnglish
Title of host publicationDatabase and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings
EditorsGünther Pernul, Sven Hartmann, Hui Ma, Abdelkader Hameurlain, Roland R. Wagner
PublisherSpringer Verlag
Pages461-470
Number of pages10
ISBN (Print)9783319988115
DOIs
StatePublished - 1 Jan 2018
Event29th International Conference on Database and Expert Systems Applications, DEXA 2018 - Regensburg, Germany
Duration: 3 Sep 20186 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11030 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference29th International Conference on Database and Expert Systems Applications, DEXA 2018
CountryGermany
CityRegensburg
Period3/09/186/09/18

Keywords

  • Data mining
  • High utility itemset
  • Trend analysis
  • Utility pattern mining

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  • Cite this

    Hackman, A., Huang, Y., & Tseng, S. (2018). Mining trending high utility itemsets from temporal transaction databases. In G. Pernul, S. Hartmann, H. Ma, A. Hameurlain, & R. R. Wagner (Eds.), Database and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings (pp. 461-470). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11030 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-98812-2_42