Mining high utility itemsets in big data

Ying Chun Lin*, Cheng Wei Wu, Vincent S. Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

46 Scopus citations


In recent years, extensive studies have been conducted on high utility itemsets (HUI) mining with wide applications. However, most of them assume that data are stored in centralized databases with a single machine performing the mining tasks. Consequently, existing algorithms cannot be applied to the big data environments, where data are often distributed and too large to be dealt with by a single machine. To address this issue, we propose a new framework for mining high utility itemsets in big data. A novel algorithm named PHUI-Growth (Parallel mining High Utility Itemsets by pattern-Growth) is proposed for parallel mining HUIs on Hadoop platform, which inherits several nice properties of Hadoop, including easy deployment, fault recovery, low communication overheads and high scalability. Moreover, it adopts the MapReduce architecture to partition the whole mining tasks into smaller independent subtasks and uses Hadoop distributed file system to manage distributed data so that it allows to parallel discover HUIs from distributed data across multiple commodity computers in a reliable, fault tolerance manner. Experimental results on both synthetic and real datasets show that PHUI-Growth has high performance on large-scale datasets and outperforms state-of-the-art non-parallel type of HUI mining algorithms.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings
EditorsTru Cao, Ee-Peng Lim, Tu-Bao Ho, Zhi-Hua Zhou, Hiroshi Motoda, David Cheung
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319180311
StatePublished - 1 Jan 2015
Event19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015 - Ho Chi Minh City, Viet Nam
Duration: 19 May 201522 May 2015

Publication series

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


Conference19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015
CountryViet Nam
CityHo Chi Minh City


  • Big data analytics
  • Hadoop platform
  • High utility itemset mining

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