Name
Abstract | ||
|---|---|---|
•We propose a novel approach to concurrent relational association rule mining.•Experiments show significant time reduction compared to the classical mining method.•The algorithm is faster with 52:3% (in average) than the classical mining method. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.eswa.2019.01.082 | Expert Systems with Applications |
Keywords | Field | DocType |
Data mining,Relational association rules,Concurrency | Data mining,Computer science,Source data,Concurrency,Complex data type,Curse of dimensionality,Association rule learning,Artificial intelligence,Business process discovery,Machine learning | Journal |
Volume | ISSN | Citations |
125 | 0957-4174 | 1 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriela Czibula | 1 | 80 | 19.53 |
| István Gergely Czibula | 2 | 91 | 11.79 |
| Diana-Lucia Miholca | 3 | 7 | 3.47 |
| Liana Maria Crivei | 4 | 1 | 1.70 |