Name
Abstract | ||
|---|---|---|
•Instead of dealing with the Boolean attributes, we mainly represent the temporal relation among numerical attributes.•The algorithm is presented to mine the multidimensional temporal association rules.•A new structure called frequent itemsets tree is proposed to avoid from generating candidate item set in mining rules.•Building the tree and mining the temporal relation between the frequent itemset proceed simultaneously, which provides better mining efficiency and interpretability. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.asoc.2017.09.013 | Applied Soft Computing |
Keywords | Field | DocType |
Temporal relationship,Frequent itemsets tree,Temporal association rule,Interpretability | Data mining,Interpretability,Association rule learning,Rule mining,Artificial intelligence,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
62 | 1568-4946 | 7 |
PageRank | References | Authors |
0.49 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ling Wang | 1 | 8 | 1.51 |
| Jianyao Meng | 2 | 10 | 1.20 |
| Peipei Xu | 3 | 7 | 2.85 |
| Kaixiang Peng | 4 | 53 | 12.22 |