Continuous k-means monitoring over moving objects

Zhenjie Zhang, Yin Yang, Anthony K.H. Tung, Dimitris Papadias

Research output: Contribution to journalArticle

34 Citations (Scopus)


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.

Original languageEnglish
Article number4479463
Pages (from-to)1205-1216
Number of pages12
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number9
Publication statusPublished - Sep 2008
Externally publishedYes



  • k-means, continuous monitoring, query processing

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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