An Improved MapReduce Algorithm for Mining Closed Frequent Itemsets

Yaron Gonen, Ehud Gudes

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

5 Scopus citations

Abstract

Mining closed frequent item sets is a key objective in the field of data mining due to its wide range of applications. Given a database of transactions, the task is to find closed subsets which appear frequently in different transactions. This subject has been studied thoroughly, and many efficient algorithms had been presented, however, most of them were designed for a non-distributed setting. The exponential growth of data in current times forces storing it in a distributed setting, meaning that most algorithms no longer apply. MapReduce is an acclaimed programming paradigm for processing large-scale, distributed data. In this paper we present an efficient algorithm for mining closed frequent item sets using the MapReduce paradigm. In addition to its novelty of running in a distributed setting, it also makes the duplication elimination step - a common step to all existing algorithms - redundant.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Software Science, Technology and Engineering, SwSTE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages77-83
Number of pages7
ISBN (Electronic)9781509010189
DOIs
StatePublished - 18 Jul 2016
Event2016 IEEE International Conference on Software Science, Technology and Engineering, SwSTE 2016 - Beer Sheva, Israel
Duration: 23 Jun 201624 Jun 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Software Science, Technology and Engineering, SwSTE 2016

Conference

Conference2016 IEEE International Conference on Software Science, Technology and Engineering, SwSTE 2016
Country/TerritoryIsrael
CityBeer Sheva
Period23/06/1624/06/16

Keywords

  • closed itemsets
  • data mining
  • frequent itemsets

ASJC Scopus subject areas

  • Modeling and Simulation
  • Software

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