A survey of itemset mining

Fournier-Viger, Philippe and Lin, Jerry Chun-Wei and Vo, Bay and Chi, Tin Truong and Zhang, Ji ORCID: https://orcid.org/0000-0001-7167-6970 and Le, Hoai Bac (2017) A survey of itemset mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (DMKD), 7 (4):e1207. ISSN 1942-4787


Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently cooccurring in databases. Because of its numerous applications in domains such as bioinformatics, text mining, product recommendation, e-learning, and web click stream analysis, itemset mining has become a popular research area. This study provides an up-to-date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics are provided. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high-utility itemset mining, rare itemset mining, fuzzy itemset mining, and uncertain itemset mining. This study also discusses research opportunities and the relationship to other popular pattern mining problems, such as sequential pattern mining, episode mining, subgraph mining, and association rule mining. Main open-source libraries of itemset mining implementations are also briefly presented. WIREs Data Mining Knowl Discov 2017, 7:e1207. doi: 10.1002/widm.1207

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Crop Health (24 Mar 2014 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Crop Health (24 Mar 2014 -)
Date Deposited: 20 Oct 2017 01:24
Last Modified: 01 Apr 2021 06:01
Uncontrolled Keywords: itemset mining; data mining; databases;
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
Identification Number or DOI: https://doi.org/10.1002/widm.1207
URI: http://eprints.usq.edu.au/id/eprint/31178

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