Towards Efficient Discovery of Periodic-Frequent Patterns in Dense Temporal Databases Using Complements
Paper
Paper/Presentation Title | Towards Efficient Discovery of Periodic-Frequent Patterns in Dense Temporal Databases Using Complements |
---|---|
Presentation Type | Paper |
Authors | Veena, P., Sreepada, Tarun, Kiran, R. Uday, Dao, Minh-Son, Zettsu, Koji, Watanobe, Yutaka and Zhang, Ji |
Journal or Proceedings Title | Proceedings of 33rd International Conference on Database and Expert Systems Applications (DEXA 2022) |
Journal Citation | 13427, pp. 204-215 |
Number of Pages | 12 |
Year | 2022 |
Publisher | Springer |
Place of Publication | Switzerland |
ISBN | 9783031124259 |
9783031124266 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-031-12426-6_16 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-031-12426-6_16 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-031-12426-6 |
Conference/Event | 33rd International Conference on Database and Expert Systems Applications (DEXA 2022) |
Event Details | 33rd International Conference on Database and Expert Systems Applications (DEXA 2022) Parent International Conference on Database and Expert Systems Applications Delivery In person Event Date 22 to end of 24 Aug 2022 Event Location Vienna, Austria |
Abstract | Periodic-frequent pattern mining involves finding all periodically occurring patterns in a temporal database. Most previous studies found these patterns by storing the temporal occurrence information of an item in a list structure. Unfortunately, this approach makes pattern mining computationally expensive on dense databases due to increased list sizes. With this motivation, this paper explores the concept of complements, and proposes an efficient algorithm that records non-occurrence information of an item to find all desired patterns in a dense database. Experimental results demonstrate that our algorithm is efficient. |
Keywords | Temporal databases; Periodic patterns; Data mining |
ANZSRC Field of Research 2020 | 460599. Data management and data science not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Series | Lecture Notes in Computer Science |
Byline Affiliations | SBRIT, India |
University of Aizu, Japan | |
National Institute of Information and Communications Technology, Japan | |
University of Southern Queensland |
https://research.usq.edu.au/item/z58zy/towards-efficient-discovery-of-periodic-frequent-patterns-in-dense-temporal-databases-using-complements
30
total views1
total downloads0
views this month0
downloads this month