Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set compliments
Article
Veena, Pamalla, Sreepada, Tarun, Kiran, Rage Uday, Dao, Minh-Son, Zettsu, Koli, Watanobe, Yutaka and Zhang, Ji. 2023. "Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set compliments." IEEE Access. 11, pp. 118676-118688. https://doi.org/10.1109/ACCESS.2023.3326419
| Article Title | Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set compliments |
|---|---|
| ERA Journal ID | 210567 |
| Article Category | Article |
| Authors | Veena, Pamalla, Sreepada, Tarun, Kiran, Rage Uday, Dao, Minh-Son, Zettsu, Koli, Watanobe, Yutaka and Zhang, Ji |
| Journal Title | IEEE Access |
| Journal Citation | 11, pp. 118676-118688 |
| Number of Pages | 13 |
| Year | 2023 |
| Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
| Place of Publication | United States |
| ISSN | 2169-3536 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2023.3326419 |
| Web Address (URL) | https://ieeexplore.ieee.org/abstract/document/10288493 |
| Abstract | Periodic-frequent patterns are a vital class of regularities in a temporal database. Most previous studies followed the approach of finding these patterns by storing the temporal occurrence information of a pattern in a list. While this approach facilitates the existing algorithms to be practicable on sparse databases, it also makes them impracticable (or computationally expensive) on dense databases due to increased list sizes. A renowned concept in set theory is that the larger the set, the smaller its complement will be. Based on this conceptual fact, this paper explores the complements, redefines the periodic-frequent pattern and proposes an efficient depth-first search algorithm that finds all periodic-frequent patterns by storing only non-occurrence information of a pattern in a database. Experimental results on several databases demonstrate that our algorithm is efficient. |
| Keywords | data mining; pattern mining; periodic patterns; set complemen; temporal databases |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 461299. Software engineering not elsewhere classified |
| Byline Affiliations | Jawaharlal Nehru Technological University Anantapur, India |
| University of Aizu, Japan | |
| National Institute of Information and Communications Technology, Japan | |
| School of Mathematics, Physics and Computing |
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