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Clustering Very Large Data Sets with Principal Direction Divisive Partitioning

  • D. Littau
  • D. Boley
Chapter

Summary

We present a method to cluster data sets too large to fit in memory, based on a Low-Memory Factored Representation (LMFR). The LMFR represents the original data in a factored form with much less memory, while preserving the individuality of each of the original samples. The scalable clustering algorithm Principal Direction Divisive Partitioning (PDDP) can use the factored form in a natural way to obtain a clustering of the original dataset.

The resulting algorithm is the PieceMeal PDDP (PMPDDP) method. The scalability of PMPDDP is demonstrated with a complexity analysis and experimental results. A discussion on the practical use of this method by a casual user is provided.

Keywords

Data Item Cluster Quality Section Representative Root Cluster Representative Vector 
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

  • D. Littau
    • 1
  • D. Boley
    • 1
  1. 1.University of MinnesotaMinneapolisUSA

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