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Issue No. 06 - June (2015 vol. 27)
ISSN: 1041-4347
pp: 1505-1518
Shuo Shang , State Key Laboratory of Petroleum Resources and Prospecting, Department of Computer Science, China University of Petroleum, Beijing, P.R. China
Kai Zheng , School of Information Technology & Electrical Engineering, The University of Queensland, Brisbane, Australia
Christian S. Jensen , Department of Computer Science, Aalborg University, Aalborg East, Denmark
Bin Yang , Department of Computer Science, Aalborg University, Aalborg East, Denmark
Panos Kalnis , , King Abdullah University of Science and Technology, Saudi Arabia
Guohe Li , State Key Laboratory of Petroleum Resources and Prospecting, Department of Computer Science, China University of Petroleum, Beijing, P.R. China
Ji-Rong Wen , Key Laboratory of Data Engineering and Knowledge Engineering MOE, Renmin University of China, P.R. China
ABSTRACT
The discovery of regions of interest in large cities is an important challenge. We propose and investigate a novel query called the path nearby cluster (PNC) query that finds regions of potential interest (e.g., sightseeing places and commercial districts) with respect to a user-specified travel route. Given a set of spatial objects $_$O$_$ (e.g., POIs, geo-tagged photos, or geo-tagged tweets) and a query route $_$q$_$ , if a cluster $_$c$_$ has high spatial-object density and is spatially close to $_$q$_$ , it is returned by the query (a cluster is a circular region defined by a center and a radius). This query aims to bring important benefits to users in popular applications such as trip planning and location recommendation. Efficient computation of the PNC query faces two challenges: how to prune the search space during query processing, and how to identify clusters with high density effectively. To address these challenges, a novel collective search algorithm is developed. Conceptually, the search process is conducted in the spatial and density domains concurrently. In the spatial domain, network expansion is adopted, and a set of vertices are selected from the query route as expansion centers. In the density domain, clusters are sorted according to their density distributions and they are scanned from the maximum to the minimum. A pair of upper and lower bounds are defined to prune the search space in the two domains globally. The performance of the PNC query is studied in extensive experiments based on real and synthetic spatial data.
INDEX TERMS
Equations, Query processing, Clustering algorithms, Roads, Educational institutions, Upper bound, Planning
CITATION

S. Shang et al., "Discovery of Path Nearby Clusters in Spatial Networks," in IEEE Transactions on Knowledge & Data Engineering, vol. 27, no. 6, pp. 1505-1518, 2015.
doi:10.1109/TKDE.2014.2382583
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