Name
Abstract | ||
|---|---|---|
Temporal data mining is still one of important research topic since there are application areas that need knowledge from temporal data such as sequential patterns, similar time sequences, cyclic and temporal association rules, and so on. Although there are many studies for temporal data mining, they do not deal with discovering knowledge from temporal interval data such as patient histories, purchaser histories, and web logs etc. We propose a new temporal data mining technique that can extract temporal interval relation rules from temporal interval data by using Allen's theory: a preprocessing algorithm designed for the generalization of temporal interval data and a temporal relation algorithm for mining temporal relation rules from the generalized temporal interval data. This technique can provide more useful knowledge in comparison with conventional data mining techniques. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.jss.2008.07.037 | Journal of Systems and Software |
Keywords | Field | DocType |
temporal interval relational rule,temporal interval relation rule,data mining,temporal interval data,rule mining,temporal association rule,temporal relation algorithm,conventional data mining technique,interval temporal mining,temporal data mining,generalized temporal interval data,new temporal data mining,temporal data,temporal relation rule,algorithm design,association rule | Data mining,Temporal interval,Pattern recognition,Computer science,Association rule learning,Temporal database,Rule mining,Artificial intelligence,Temporal data mining,Preprocessing algorithm | Journal |
Volume | Issue | ISSN |
82 | 1 | The Journal of Systems & Software |
Citations | PageRank | References |
10 | 0.65 | 38 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Joon Lee | 1 | 25 | 2.27 |
| Jun Wook Lee | 2 | 55 | 5.45 |
| Duck Jin Chai | 3 | 18 | 2.24 |
| Buhyun Hwang | 4 | 112 | 24.15 |
| Keun Ho Ryu | 5 | 883 | 85.61 |