An efficient closed frequent itemset miner for the moa stream mining system

Massimo Quadrana, Albert Bifet, Ricard Gavaldà

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

Abstract

We describe and evaluate an implementation of the IncMine algorithm due to Cheng, Ke, and Ng (2008) for mining frequent closed itemsets from data streams, working on the MOA platform. The goal was to produce a robust, efficient, and usable tool for that task that can both be used by practitioners and used for evaluation of research in the area. We experimentally confirm the excellent performance of the algorithm and its ability to handle concept drift.

Original languageEnglish
Title of host publicationArtificial Intelligence Research and Development. Proceedings of the 16th International Conference of the Catalan Association for Artificial Intelligence
EditorsKarina Gibert, Vicent Botti, Ramon Reig-Bolano
PublisherIOS Press BV
Pages203-212
Number of pages10
ISBN (Print)9781614993193
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume256
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Keywords

  • Data mining
  • Data streams
  • Itemset mining
  • MOA
  • Stream mining

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