Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/6001

Continuous k-means monitoring over moving objects

Authors Zhang, Zhenjie
Yang, Yin
Tung, Anthony K. H.
Papadias, Dimitris
Issue Date 2008
Source IEEE transactions on knowledge and data engineering, v. 20, (9), 2008, September, p. 1205-1216
Summary Given a data set P, a k-means query returns k points in space (called centers), such that the average squared distance between each point in P and its nearest center is minimized. Since this problem is NP-hard, several approximate algorithms have been proposed and used in practice. In this paper, we study continuous k-means computation at a server that monitors a set of moving objects. Reevaluating k-means every time there is an object update imposes a heavy burden on the server (for computing the centers from scratch) and the clients (for continuously sending location updates). We overcome these problems with a novel approach that significantly reduces the computation and communication costs, while guaranteeing that the quality of the solution, with respect to the reevaluation approach, is bounded by a user-defined tolerance. The proposed method assigns each moving object a threshold (i.e., range) such that the object sends a location update only when it crosses the range boundary. First, we develop an efficient technique for maintaining the k-means. Then, we present mathematical formulas and algorithms for deriving the individual thresholds. Finally, we justify our performance claims with extensive experiments.
Subjects
ISSN 1041-4347
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Language English
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