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Unsupervised Rare Pattern Mining: A Survey

Authors:

Sri Devi RavanaAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 10, Issue 4

Article No.: 45, Pages 1 - 29

https://doi.org/10.1145/2898359

Published: 24 May 2016 Publication History

Abstract

Association rule mining was first introduced to examine patterns among frequent items. The original motivation for seeking these rules arose from need to examine customer purchasing behaviour in supermarket transaction data. It seeks to identify combinations of items or itemsets, whose presence in a transaction affects the likelihood of the presence of another specific item or itemsets. In recent years, there has been an increasing demand for rare association rule mining. Detecting rare patterns in data is a vital task, with numerous high-impact applications including medical, finance, and security. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for rare pattern mining. We investigate the problems in finding rare rules using traditional association rule mining. As rare association rule mining has not been well explored, there is still specific groundwork that needs to be established. We will discuss some of the major issues in rare association rule mining and also look at current algorithms. As a contribution, we give a general framework for categorizing algorithms: Apriori and Tree based. We highlight the differences between these methods. Finally, we present several real-world application using rare pattern mining in diverse domains. We conclude our survey with a discussion on open and practical challenges in the field.

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Index Terms

Unsupervised Rare Pattern Mining: A Survey
1. General and reference
  1. Document types
    1. Surveys and overviews
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 10, Issue 4

Special Issue on SIGKDD 2014, Special Issue on BIGCHAT and Regular Papers

July 2016

417 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/2936311

Editor:
Philip S. Yu
University of Illinois at Chicago, USA

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Copyright © 2016 ACM.

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Association for Computing Machinery

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Publication History

Published: 24 May 2016

Accepted: 01 February 2016

Revised: 01 October 2015

Received: 01 December 2014

Published in TKDD Volume 10, Issue 4

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University of Auckland (FRDF)
University of Malaya and Ministry of Education (High Impact Research)

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Demirbaga ÜAujla GJindal AKalyon ODemirbaga ÜAujla GJindal AKalyon O(2024)Machine Learning for Big Data AnalyticsBig Data Analytics10.1007/978-3-031-55639-5_9(193-231)Online publication date: 8-May-2024
https://doi.org/10.1007/978-3-031-55639-5_9
Li YZhang CLi JSong WQi ZWu YWu X(2023)MCoR-Miner: Maximal Co-Occurrence Nonoverlapping Sequential Rule MiningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.324121335:9(9531-9546)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3241213
Gan WChen LWan SChen JChen C(2023)Anomaly Rule Detection in Sequence DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313908635:12(12095-12108)Online publication date: 1-Dec-2023
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