Name
Abstract | ||
|---|---|---|
The demand for and dependence on social networks make the virtual world returning to real life along with real time, actual space and concrete events. To create joints from online topics to offline activities, a spatio-temporal and structure feature model is established by fusing the background information, and then the topics are investigated by clustering the keywords. Compared with the traditional methods, background future clustering keeps the constrains caused by data sparseness and spatio-temporal dependence off, and can be used for unpredictable activities discovery. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ipl.2018.03.017 | Information Processing Letters |
Keywords | Field | DocType |
Algorithms,Social media,Short text,Spatio-temporal characteristics,Feature fusion | Discrete mathematics,Social network,Feature model,Artificial intelligence,Cluster analysis,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
136 | 0020-0190 | 0 |
PageRank | References | Authors |
0.34 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuangying Zhu | 1 | 0 | 0.68 |
| Junping Du | 2 | 789 | 91.80 |