Name
Abstract | ||
|---|---|---|
In this paper, we describe how feature clustering on real-world cell-phone data can be used to locate the impact area of emergency events. We first examine the effect of two emergency events on the call activity in the areas surrounding the events. We then investigate how the time series of the affected areas behave relative to the time series of their respective neighboring areas. Finally, we examine the differences in hierarchical clusterings of the time series of the affected areas and neighboring areas. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-01973-9_52 | ICCS (2) |
Keywords | Field | DocType |
dynamic data driven application,hierarchical clusterings,respective neighboring area,time series,data steering,emergency event,impact area,call activity,feature clustering,real-world cell-phone data,neighboring area,affected area,hierarchical clustering | Data mining,Computer science,Dynamic data,Artificial intelligence,Cluster analysis,Machine learning | Conference |
Volume | ISSN | Citations |
5545 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 12 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alec Pawling | 1 | 41 | 4.08 |
| Greg Madey | 2 | 153 | 9.65 |