Name
Abstract | ||
|---|---|---|
An overview of the Time Series Knowledge Mining framework to discover knowledge in multivariate time series is given. A hierarchy of temporal patterns, which are not a priori given, is discovered. The patterns are based on the rule language Unification-based Temporal Grammar. A semiotic hierarchy of temporal concepts is build in a bottom up manner from multivariate time instants. We describe the mining problem for each rule discovery step. Several of the steps can be performed with well known data mining algorithms. We present novel algorithms that perform two steps not covered by existing methods. First results on a dataset describing muscle activity during sports are presented. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/3-540-28084-7_30 | Studies in Classification Data Analysis and Knowledge Organization |
Keywords | Field | DocType |
time series,bottom up | Data mining,Semiotics,Multivariate statistics,Computer science,Unification,Top-down and bottom-up design,A priori and a posteriori,Grammar,Data mining algorithm,Hierarchy | Conference |
ISSN | Citations | PageRank |
1431-8814 | 12 | 0.88 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabian Mörchen | 1 | 372 | 17.94 |
| Alfred Ultsch | 2 | 403 | 51.77 |