Name
Abstract | ||
|---|---|---|
Sequential pattern mining is one of the important techniques of data mining to discover some potential useful knowledge from large databases. However, existing approaches for mining sequential patterns are designed for point-based events. In many applications, the essence of events are interval-based, such as disease suffered, stock price increase or decrease, chatting etc. This paper presents a new algorithm to discover temporal pattern from temporal sequences database consisting of interval-based events. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11881599_70 | FSKD |
Keywords | Field | DocType |
large databases,temporal sequences database,sequential pattern mining,mining sequential pattern,new algorithm,sequence database,temporal pattern,potential useful knowledge,point-based event,important technique,interval-based event,data mining | Data mining,Regular expression,Sequence database,Computer science,Temporal database,Information extraction,Knowledge extraction,Artificial intelligence,Knowledge base,Reactive system,Machine learning,Design pattern | Conference |
Volume | ISSN | ISBN |
4223 | 0302-9743 | 3-540-45916-2 |
Citations | PageRank | References |
4 | 0.39 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yen-Liang Chen | 1 | 1361 | 73.85 |
| Shin-Yi Wu | 2 | 414 | 31.59 |