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Characterizing transactional databases for frequent itemset mining

Author
Lezcano, C.; Arias, M.
Type of activity
Presentation of work at congresses
Name of edition
1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning
Date of publication
2019
Presentation's date
2019-05-04
Book of congress proceedings
Proceedings of the 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning: co-located with SIAM International Conference on Data Mining (SDM 2019), Calgary, Alberta, Canada, May 4th, 2019
First page
44
Last page
53
Publisher
CEUR-WS.org
Project funding
Management and Analysis of Complex DATA
Repository
http://hdl.handle.net/2117/192797 Open in new window
URL
http://ceur-ws.org/Vol-2436/article_5.pdf Open in new window
Abstract
This paper presents a study of the characteristics of transactional databases used in frequent itemset mining. Such characterizations have typically been used to benchmark and understand the data mining algorithms working on these databases. The aim of our study is to give a picture of how diverse and representative these benchmarking databases are, both in general but also in the context of particular empirical studies found in the literature. Our proposed list of metrics contains many of the e...
Citation
Lezcano, C.; Arias, M. Characterizing transactional databases for frequent itemset mining. A: SIAM International Conference on Data Mining. "Proceedings of the 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning: co-located with SIAM International Conference on Data Mining (SDM 2019), Calgary, Alberta, Canada, May 4th, 2019". CEUR-WS.org, 2019, p. 44-53.
Keywords
Data characterization, Frequent itemset mining, Transactional databases
Group of research
LARCA - Laboratory of Relational Algorithmics, Complexity and Learnability

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