Mining high-utility itemsets with both positive and negative unit profits from uncertain databases

Wensheng Gan, Jerry Chun Wei Lin*, Philippe Fournier-Viger, Han Chieh Chao, S. Tseng

*Corresponding author for this work

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

10 Scopus citations

Abstract

Some important limitation of frequent itemset mining are that it assumes that each item cannot appear more than once in each transaction, and all items have the same importance (weight, cost, risk, unit profit or value). These assumptions often do not hold in real-world applications. For example, consider a database of customer transactions containing information about the purchase quantities of items in each transaction and the positive or negative unit profit of each item. Besides, uncertainty is commonly embedded in collected data in real-life applications. To address this issue, we propose an efficient algorithm named HUPNU (mining High-Utility itemsets with both Positive and Negative unit profits from Uncertain databases), the high qualified patterns can be discovered effectively for decision-making. Based on the designed vertical PU±-list (Probability-Utility list with Positive-and-Negative profits) structure and several pruning strategies, HUPNU can directly discovers the potential high-utility itemsets without generating candidates.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings
EditorsKyuseok Shim, Jae-Gil Lee, Longbing Cao, Xuemin Lin, Jinho Kim, Yang-Sae Moon
PublisherSpringer Verlag
Pages434-446
Number of pages13
ISBN (Print)9783319574530
DOIs
StatePublished - 1 Jan 2017
Event21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 - Jeju, Korea, Republic of
Duration: 23 May 201726 May 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10234 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017
CountryKorea, Republic of
CityJeju
Period23/05/1726/05/17

Keywords

  • Frequent itemset
  • Negative unit profit
  • PU-list
  • Uncertainty

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    Gan, W., Lin, J. C. W., Fournier-Viger, P., Chao, H. C., & Tseng, S. (2017). Mining high-utility itemsets with both positive and negative unit profits from uncertain databases. In K. Shim, J-G. Lee, L. Cao, X. Lin, J. Kim, & Y-S. Moon (Eds.), Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings (pp. 434-446). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10234 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-57454-7_34