A Novel Clustering Method Based on Spatial Operations

  • Hui Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4042)


In this paper we present a novel clustering method that can deal with both numerical and categorical data with a novel clustering objective and without the need of a user specified parameter. Our approach is based on an extension of database relation – hyperrelations. A hyperrelation is a set of hypertuples, which are vectors of sets.

In this paper we show that hyperrelations can be exploited to develop a new method for clustering both numerical and categorical data. This method merges hypertuples pairwise in the direction of increasing the density of hypertuples. This process is fully automatic in the sense that no parameter is needed from users. Initial experiments with artificial and real-world data showed this novel approach is promising.


Association Rule Data Object Categorical Attribute Optimal Cluster Domain Lattice 
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 2006

Authors and Affiliations

  • Hui Wang
    • 1
  1. 1.School of Computing and MathematicsUniversity of Ulster at JordanstownNewtownabbey, Northern IrelandUK

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