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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Littau, D., Boley, D. (2006). Clustering Very Large Data Sets with Principal Direction Divisive Partitioning. In: Kogan, J., Nicholas, C., Teboulle, M. (eds) Grouping Multidimensional Data. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28349-8_4
Download citation
DOI: https://doi.org/10.1007/3-540-28349-8_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28348-5
Online ISBN: 978-3-540-28349-2
eBook Packages: Computer ScienceComputer Science (R0)