Name
Abstract | ||
|---|---|---|
SB-Tree is a data structure proposed to represent time series according to the importance of the data points. Its advantages over traditional time series representation approaches include: representing time series directly in time domain (shape preservation), retrieving time series data according to the importance of the data points and facilitating multi-resolution time series retrieval. Based on these benefits, one may find this representation particularly attractive in financial time series domain and the corresponding data mining tasks, i.e. categorization and clustering. In this paper, an investigation on the size of the SB-Tree is reported. Two SB-Tree optimization approaches are proposed to reduce the size of the SB-Tree while the overall shape of the time series can be preserved. As demonstrated by various experiments, the proposed approach is suitable for different categorization and clustering applications. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11881599_141 | FSKD |
Keywords | Field | DocType |
stock time series categorization,time domain,time series,multi-resolution time series retrieval,sb-tree optimization approach,traditional time series representation,retrieving time series data,data point,corresponding data mining task,data structure,financial time series domain,data mining,time series data | Data mining,Time series,Computer science,Artificial intelligence,Cluster analysis,Data point,Time domain,Data structure,Categorization,Pattern recognition,Tree (data structure),Knowledge extraction,Machine learning | Conference |
Volume | ISSN | ISBN |
4223 | 0302-9743 | 3-540-45916-2 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tak-chung Fu | 1 | 407 | 21.29 |
| Chi-wai Law | 2 | 0 | 0.34 |
| Kin-kee Chan | 3 | 0 | 0.34 |
| Fu Lai Chung | 4 | 1534 | 86.72 |
| Chak-man Ng | 5 | 116 | 9.33 |