Publication
Title
Frequent itemset mining for big data
Author
Abstract
Frequent Itemset Mining (FIM) is one of the most well known techniques to extract knowledge from data. The combinatorial explosion of FIM methods become even more problematic when they are applied to Big Data. Fortunately, recent improvements in the field of parallel programming already provide good tools to tackle this problem. However, these tools come with their own technical challenges, e.g. balanced data distribution and inter-communication costs. In this paper, we investigate the applicability of FIM techniques on the MapReduce platform. We introduce two new methods for mining large datasets: Dist-Eclat focuses on speed while BigFIM is optimized to run on really large datasets. In our experiments we show the scalability of our methods.
Language
English
Source (book)
IEEE Big Data 2013 : International Conference on Big Data, October 6-9, 2013, Santa Clara, Calif., USA
Publication
New York, N.Y. : IEEE, 2013
ISBN
978-1-4799-1292-6
Volume/pages
p. 111-118
ISI
000330831300199
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 30.01.2014
Last edited 06.11.2017
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