A survey of itemset mining
Article
Article Title | A survey of itemset mining |
---|---|
ERA Journal ID | 201715 |
Article Category | Article |
Authors | Fournier-Viger, Philippe (Author), Lin, Jerry Chun-Wei (Author), Vo, Bay (Author), Chi, Tin Truong (Author), Zhang, Ji (Author) and Le, Hoai Bac (Author) |
Journal Title | WIREs Data Mining and Knowledge Discovery |
Journal Citation | 7 (4) |
Article Number | e1207 |
Number of Pages | 18 |
Year | 2017 |
Publisher | John Wiley & Sons |
Place of Publication | United Kingdom |
ISSN | 1942-4787 |
1942-4795 | |
Digital Object Identifier (DOI) | https://doi.org/10.1002/widm.1207 |
Abstract | 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 |
Keywords | itemset mining; data mining; databases; |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Peking University, China |
Harbin Institute of Technology, China | |
Vietnam National University, Vietnam | |
Dalat University, Vietnam | |
Faculty of Health, Engineering and Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q3z65/a-survey-of-itemset-mining
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