Transaction Clustering Using a Seeds Based Approach

  • Yun Sing Koh
  • Russel Pears
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5012)


Transaction clustering has received a great deal of attention in the past few years. Its functionality extends well beyond traditional clustering algorithms which basically perform a near-neighbourhood search for locating groups of similar instances. The basic concept underlying transaction clustering stems from the concept of large items as defined by association rule mining algorithms. Clusters formed on the basis of large items that are shared between instances offer an attractive alternative to association rule mining systems. Currently, none of the techniques proposed offer a good solution to scenarios where large items overlap across clusters. In this paper we overcome the aforementioned limitations by using cluster seeds that represent initial centroids. Seeds are generated from sets of transaction items that occur together above a certain threshold and such seeds may overlap in their itemsets across clusters.


Frequent Item Relative Support Cluster Centroid Large Item Traditional Cluster 
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 2008

Authors and Affiliations

  • Yun Sing Koh
    • 1
  • Russel Pears
    • 1
  1. 1.School of Computing and Mathematical SciencesAuckland University of TechnologyNew Zealand

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