Constraint-Based Clustering in Large Databases

  • Anthony K. H. Tung
  • Jiawei Han
  • Laks V.S. Lakshmanan
  • Raymond T. Ng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1973)


Constrained clustering — finding clusters that satisfy user-specified constraints — is highly desirable in many applications. In this paper, we introduce the constrained clustering problem and show that traditional clustering algorithms (e.g., k-means) cannot handle it. A scalable constraint-clustering algorithm is developed in this study which starts by finding an initial solution that satisfies user-specified constraints and then refines the solution by performing confined object movements under constraints. Our algorithm consists of two phases: pivot movement and deadlock resolution. For both phases, we show that finding the optimal solution is NP-hard. We then propose several heuristics and show how our algorithm can scale up for large data sets using the heuristic of micro-cluster sharing. By experiments, we show the effectiveness and efficiency of the heuristics.


Movement Path Spatial Data Mining Deadlock Resolution Average Average Average Pivot Movement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Anthony K. H. Tung
    • 1
  • Jiawei Han
    • 1
  • Laks V.S. Lakshmanan
    • 2
  • Raymond T. Ng
    • 3
  1. 1.Simon Fraser UniversityCanada
  2. 2.IITBombay & Concordia U
  3. 3.University of British ColumbiaCanada

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