Name
Abstract | ||
|---|---|---|
Clustering is an important technology in mining time series, and the key is to define the similarity or dissimilarity between data.One of existing time series distance measures LB_Keogh, is tighter lower bounding than Euclidean and Dynamic Time Warping (DTW), however, it is an asymmetrical distance measure, and has its limitation in clustering.To solve the problem, we present a symmetrical boundary distance measure called LB_HUST, and prove that it is tighter lower bounding than LB_Keogh. We apply LBJIUST to cluster time series, and update the boundary of the cluster when a new time series is added into the cluster. The experiments show that the method exceeds the approaches based on Euclidean and DTW in terms of accuracy. © 2006 IEEE. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICIT.2006.63 | Proceedings - 9th International Conference on Information Technology, ICIT 2006 |
Keywords | DocType | Volume |
null | Conference | null |
Issue | ISSN | ISBN |
null | null | 0-7695-2635-7 |
Citations | PageRank | References |
2 | 0.37 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junkui Li | 1 | 2 | 0.37 |
| Yuanzhen Wang | 2 | 86 | 11.78 |
| Xinping Li | 3 | 2 | 0.37 |