Towards fast and memory efficient discovery of periodic frequent patterns-Reference-Cited by-同舟云学术

Towards fast and memory efficient discovery of periodic frequent patterns

Published:2019-07-10 Issue:4 Volume:3 Page:480-493
ISSN:2475-1839
Container-title:Journal of Information and Telecommunication
language:en
Short-container-title:Journal of Information and Telecommunication

Author:

Nofong Vincent Mwintieru¹^ORCID,Wondoh John²^ORCID

Affiliation:

1. Faculty of Engineering, University of Mines and Technology, Tarkwa, Ghana

2. School of Information Technology and Mathematical Sciences, University of South of Australia, Adelaide, Australia

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Computer Science Applications,Computer Science (miscellaneous)

Link

https://tandfonline.com/doi/pdf/10.1080/24751839.2019.1634868

Reference22 articles.

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3. Fournier-Viger, P., Lin, J. C. W., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z. & Lam, H. T. (2016). The SPMF open-source data mining library version 2. Proceedings of European conference on machine learning and knowledge discovery in databases (pp. 36–40). Cham: Springer.

4. Han, J., Pei, J. & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Rec. (Vol. 29(2), pp. 1–12). ACM.

5. Kiran, R. U. & Kitsuregawa, M. (2013). Discovering quasi-periodic-frequent patterns in transactional databases. In V. Bhatnagar, & S. Srinivasa (Eds.), BDA 2013. LNCS, Vol. 8302 (pp. 97–115). Springer International.

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