East European Conference on Advances in Databases and Information Systems

ADBIS 2015: New Trends in Databases and Information Systems pp 243-247 | Cite as

A Review of Scalable Approaches for Frequent Itemset Mining

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 539)

Abstract

Frequent Itemset Mining is a popular data mining task with the aim of discovering frequently co-occurring items and, hence, correlations, hidden in data. Many attempts to apply this family of techniques to Big Data have been presented. Unfortunately, few implementations proved to efficiently scale to huge collections of information. This review presents a comparison of a carefully selected subset of the most efficient and scalable approaches. Focusing on Hadoop and Spark platforms, we consider not only the analysis dimensions typical of the data mining domain, but also criteria to be valued in the Big Data environment.

Keywords

Frequent Itemset Mining MapReduce Spark Data mining 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Daniele Apiletti
    • 1
  • Paolo Garza
    • 1
  • Fabio Pulvirenti
    • 1
  1. 1.Dipartimento di Automatica e InformaticaPolitecnico di TorinoTorinoItaly

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