Name
Abstract | ||
|---|---|---|
We describe TimeSleuth, a hybrid tool based on the C4.5 classification software, which is intended for the discovery of temporal/causal rules. Temporally ordered data are gathered from observable attributes of a system, and used to discover relations among the attributes. In general, such rules could be atemporal or temporal. We evaluate TimeSleuth using synthetic data sets with well-known causal relations as well as real weather data. We show that by performing appropriate preprocessing and postprocessing operations, TimeSleuth extends C4.5's domain of applicability to the unsupervised discovery of temporal relations among ordered data. We compare the results obtained from TimeSleuth to those of TETRAD and CaMML, and show that TimeSleuth performs better than the other systems. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/3-540-44886-1_15 | Canadian Conference on AI |
Keywords | Field | DocType |
hybrid tool,observable attribute,unsupervised discovery,classification software,appropriate preprocessing,well-known causal relation,temporal relation,synthetic data set,causal rule,real weather data,synthetic data | Data mining,Causality,Observable,Tetrad,Causal relations,Computer science,Preprocessor,Software,Artificial intelligence,Weather data,Synthetic data sets,Machine learning | Conference |
Volume | ISSN | ISBN |
2671 | 0302-9743 | 3-540-40300-0 |
Citations | PageRank | References |
3 | 0.48 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kamran Karimi | 1 | 118 | 17.23 |
| Howard J. Hamilton | 2 | 1501 | 145.55 |