Name
Abstract | ||
|---|---|---|
Periodic detection in spatiotemporal data is one of the research focuses in data mining. Most previous works only focused on mining periodic patterns and hardly recognized misaligned presence of a pattern due to the intervention of repetitive data. A more flexible asynchronous periodic patterns mining model (AP2M2) based on clustering algorithm and SMCA algorithm is proposed which mainly has three steps, discovering the invisible repetitive data and clustering them into a single record to generate standard and usable dataset, finding the set of stopovers and mining the periodic patterns of moving objects at each stopover. In the experiment, the Chinese bird-watching data is used to check the effectiveness of AP2M2 model and the results show that the AP2M2 model can precisely mine the asynchronous periodic patterns with low time complexity. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11042-018-6752-4 | Multimedia Tools and Applications |
Keywords | Field | DocType |
Spatiotemporal data mining, asynchronous periodic pattern, clustering algorithm SMCA algorithm | USable,Asynchronous communication,Pattern recognition,Computer science,Artificial intelligence,Cluster analysis,Time complexity,Periodic graph (geometry) | Journal |
Volume | Issue | ISSN |
78 | 7 | 1573-7721 |
Citations | PageRank | References |
0 | 0.34 | 24 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shu-xia Dong | 1 | 0 | 1.35 |
| Shulei Liu | 2 | 0 | 0.34 |
| Yanyu Zhao | 3 | 0 | 0.34 |
| Zengzhen Shao | 4 | 5 | 3.28 |