Name
Title | ||
|---|---|---|
A Unified Framework to Discover Partial Periodic-Frequent Patterns in Row and Columnar Temporal Databases |
Abstract | ||
|---|---|---|
Partial periodic-frequent pattern mining is an important knowledge discovery technique in data mining. It involves identifying all frequent patterns that have exhibited partial periodic behavior in a temporal database. The following two limitations have hindered the successful industrial application of this technique: (i) there exists no algorithm to find the desired patterns in columnar temporal ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICDMW53433.2021.00080 | 2021 International Conference on Data Mining Workshops (ICDMW) |
Keywords | DocType | ISSN |
Data Mining,Knowledge discovery in databases,pattern mining,temporal databases | Conference | 2375-9232 |
ISBN | Citations | PageRank |
978-1-6654-2427-1 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pamalla Veena | 1 | 0 | 0.34 |
| So Nakamura | 2 | 0 | 0.34 |
| P. Likhitha | 3 | 0 | 0.34 |
| R. Uday Kiran | 4 | 0 | 0.68 |
| Yutaka Watanobe | 5 | 0 | 0.34 |
| Koji Zettsu | 6 | 0 | 1.69 |