Name
Abstract | ||
|---|---|---|
The Unification-Based Temporal Grammar is a temporal extension of static unification-based grammars. It defines a hierarchical temporal rule language to express complex patterns present in multivariate time series. The Temporal Data Mining Method is the accompanying framework to discover temporal knowledge based on this rule language. A semiotic hierarchy of temporal patterns, which are not a priori given, is built in a bottom up manner from static logical descriptions of multivariate time instants. We demonstrate the methods using music data, extracting typical parts of songs. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30221-6_11 | LECTURE NOTES IN COMPUTER SCIENCE |
Keywords | Field | DocType |
time series,bottom up,knowledge base | Rule-based machine translation,Data mining,Computer science,A priori and a posteriori,Temporal database,Artificial intelligence,Natural language processing,Hierarchy,Distributed computing,Multivariate statistics,Grammar,Information extraction,Knowledge extraction | Conference |
Volume | ISSN | Citations |
3238 | 0302-9743 | 8 |
PageRank | References | Authors |
0.57 | 18 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabian Mörchen | 1 | 372 | 17.94 |
| Alfred Ultsch | 2 | 403 | 51.77 |