Name
Abstract | ||
|---|---|---|
This paper proposes the concept of intervention rule which tries to reveal the interventional relationship between elements in a system in the following three aspects. (1) Casual relationship. Intervention rule shows which element is the cause and which element is the consequence. (2) Quantitative relationship: Intervention rule shows the quantitative intensity of how the change of the causal element interferes with the change of the consequential element. (3) Multi-scale intervention relationship. Intervention rule shows the intervention at different decomposition scale of the original system, since sub system may exhibit different mechanism from the original system. This paper first introduces a general intervention rule framework, and then transforms the framework into concrete intervention rules for complex network data and time series data. Then, it proposes two algorithms to mine the intervention rules from the two different systems. Finally, the experimental results show that multi-scale intervention rules do exist in real dataset. And the intervention intensity of each sub graph and sub series are always 4 or 5 time larger than intervention intensity of the original data. |
Year | Venue | Keywords |
|---|---|---|
2011 | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS | Intervention rule, Complex network, Time series, Decomposition, Multi-scale |
Field | DocType | Volume |
Time series,Artificial intelligence,Complex network,Casual,Mathematics,Machine learning | Journal | 4 |
Issue | ISSN | Citations |
4 | 1875-6891 | 0 |
PageRank | References | Authors |
0.34 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiaoling Zheng | 1 | 2 | 1.42 |
| Changjie Tang | 2 | 483 | 62.75 |
| Shaojie Qiao | 3 | 12 | 2.27 |
| Ning Yang | 4 | 0 | 1.01 |
| Yue Wang | 5 | 18 | 6.63 |