Partition-based clustering in object bases: From theory to practice

  • Carsten Gerlhof
  • Alfons Kemper
  • Christoph Kilger
  • Guido Moerkotte
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 730)


We classify clustering algorithms into sequence-based techniques—which transform the object net into a linear sequence—and partition-based clustering algorithms. Tsangaris and Naughton [TN91, TN92] have shown that the partition-based techniques are superior. However, their work is based on a single partitioning algorithm, the Kernighan and Lin heuristics, which is not applicable to realistically large object bases because of its high running-time complexity. The contribution of this paper is two-fold: (1) we devise a new class of greedy object graph partitioning algorithms (GGP) whose running-time complexity is moderate while still yielding good quality results. (2) Our extensive quantitative analysis of all well-known partitioning algorithms indicates that no one algorithm performs superior for all object net characteristics. Therefore, we propose an adaptable clustering strategy according to a multi-dimensional grid: the dimensions correspond to particular characteristics of the object base—given by, e.g., number and size of objects, degree of object sharing—and the grid entries indicate the most suitable clustering algorithm for the particular configuration.


Cluster Algorithm Object Size Object Base External Cost Cluster Graph 
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 1993

Authors and Affiliations

  • Carsten Gerlhof
    • 1
  • Alfons Kemper
    • 1
  • Christoph Kilger
    • 2
  • Guido Moerkotte
    • 2
  1. 1.Lehrstuhl für Dialogorientierte Systeme, Fakultät für Mathematik und InformatikUniversität PassauPassauGermany
  2. 2.Fakultät für InformatikUniversität KarlsruheKarlsruheGermany

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