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Synthetic dataset generation with itemset-based generative models

Author
Lezcano, C.; Arias, M.
Type of activity
Presentation of work at congresses
Name of edition
The 4th IEEE International Workshop on Reliability and Security Data Analysis
Date of publication
2019
Presentation's date
2019-10-28
Book of congress proceedings
2019 IEEE 30th International Symposium on Software Reliability Engineering Workshops: 28–31 October 2019, Berlin, Germany
First page
288
Last page
293
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
10.1109/ISSREW.2019.00086
Project funding
Management and Analysis of Complex DATA
Repository
http://hdl.handle.net/2117/193002 Open in new window
URL
https://ieeexplore.ieee.org/document/8990348 Open in new window
Abstract
This paper proposes three different data generators, tailored to transactional datasets, based on existing itemset-based generative models. All these generators are intuitive and easy to implement and show satisfactory performance. The quality of each generator is assessed by means of three different methods that capture how well the original dataset structure is preserved.
Citation
Lezcano, C.; Arias, M. Synthetic dataset generation with itemset-based generative models. A: IEEE International Symposium on Software Reliability Engineering. "2019 IEEE 30th International Symposium on Software Reliability Engineering Workshops: 28–31 October 2019, Berlin, Germany". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 288-293.
Keywords
Data generation, Generative models, Itemset mining
Group of research
LARCA - Laboratory of Relational Algorithmics, Complexity and Learnability

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