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IJMLC 2021 Vol.11(3): 208-218 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.3.1037

Modern Applications and Challenges for Rare Itemset Mining

Sadeq Darrab, David Broneske, and Gunter Saake

Abstract—Data mining is the process of extracting useful unknown knowledge from large datasets. Frequent itemset mining is the fundamental task of data mining that aims at discovering interesting itemsets that frequently appear together in a dataset. However, mining infrequent (rare) itemsets may be more interesting in many real-life applications such as predicting telecommunication equipment failures, genetics, medical diagnosis, or anomaly detection. In this paper, we survey up-to-date methods of rare itemset mining. The main goal of this survey is to provide a comprehensive overview of the state-of-the-art algorithms of rare itemset mining and its applications. The main contributions of this survey can be summarized as follows. In the first part, we define the task of rare itemset mining by explaining key concepts and terminology, motivation examples, and comparisons with underlying concepts. Then, we highlight the state-of-art methods for rare itemsets mining. Furthermore, we present variations of the task of rare itemset mining to discuss limitations of traditional rare itemset mining algorithms. After that, we highlight the fundamental applications of rare itemset mining. In the last, we point out research opportunities and challenges for rare itemset mining for future research.

Index Terms—Data mining, rare itemsets, survey.

David Broneske and Gunter Saake are with the Databases and Software Engineering Group of Gunter Saake, University of Magdeburg, Magdeburg, Germany (e-mail: dbronesk@iti.cs.uni-magdeburg.de, saake@iti.cs.uni-magdeburg.de).


Cite: Sadeq Darrab, David Broneske, and Gunter Saake, "Modern Applications and Challenges for Rare Itemset Mining," International Journal of Machine Learning and Computing vol. 11, no. 3, pp. 208-218, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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