Efficient algorithms for mining up-to-date high-utility patterns

Jerry Chun Wei Lin*, Wensheng Gan, Tzung Pei Hong, Vincent Shin-Mu Tseng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

High-utility pattern mining (HUPM) is an emerging topic in recent years instead of association-rule mining to discover more interesting and useful information for decision making. Many algorithms have been developed to find high-utility patterns (HUPs) from quantitative databases without considering timestamp of patterns, especially in recent intervals. A pattern may not be a HUP in an entire database but may be a HUP in recent intervals. In this paper, a new concept namely up-to-date high-utility pattern (UDHUP) is designed. It considers not only utility measure but also timestamp factor to discover the recent HUPs. The UDHUP-apriori is first proposed to mine UDHUPs in a level-wise way. Since UDHUP-apriori uses Apriori-like approach to recursively derive UDHUPs, a second UDHUP-list algorithm is then presented to efficiently discover UDHUPs based on the developed UDU-list structures and a pruning strategy without candidate generation, thus speeding up the mining process. A flexible minimum-length strategy with two specific lifetimes is also designed to find more efficient UDHUPs based on a users' specification. Experiments are conducted to evaluate the performance of the proposed two algorithms in terms of execution time, memory consumption, and number of generated UDHUPs in several real-world and synthetic datasets.

Original languageEnglish
Article number612
Pages (from-to)648-661
Number of pages14
JournalAdvanced Engineering Informatics
Volume29
Issue number3
DOIs
StatePublished - 1 Aug 2015

Keywords

  • Data mining
  • Level-wise
  • UDU-list structures
  • Up-to-date high-utility patterns
  • Utility mining

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