Association control in mobile wireless networks
Minkyong Kim, Zhen Liu, et al.
INFOCOM 2008
Detecting significant overdensity or underdensity clusters in spatio-temporal data is critical for many real-world applica- Tions. Most existing approaches are designed to deal with regularly shaped clusters such as circular, elliptic and rect- Angular ones, but cannot work well on irregularly shaped clusters. In this paper, we propose GridScan, a grid-based approach for detecting irregularly shaped spatial clusters. In GridScan, a cluster is asymptotically described by a set of connected grid cells and is computed by a fast greedy region- growing algorithm with elaborating cluster merging in the process. The time complexity of GridScan is linear to the number of grids, making it scalable to very large datasets. A prospective spatio-temporal cluster detection approach, GridScan-Pro, is also proposed by extending GridScan. Ex- periments and a case study in the epidemic scenario demon- strate that our approaches greatly outperform existing ones in terms of accuracy, effciency, and scalability. Copyright © 2012 by the Society for Industrial and Applied Mathematics.
Minkyong Kim, Zhen Liu, et al.
INFOCOM 2008
Daniel M. Bikel, Vittorio Castelli
ACL 2008
Nanda Kambhatla
ACL 2004
Nikolaos M. Freris, Michail Vlachos, et al.
SDM 2012