Statistics and Computing

, Volume 26, Issue 5, pp 1101–1120 | Cite as

Divisive clustering of high dimensional data streams

  • David P. HofmeyrEmail author
  • Nicos G. Pavlidis
  • Idris A. Eckley


Clustering streaming data is gaining importance as automatic data acquisition technologies are deployed in diverse applications. We propose a fully incremental projected divisive clustering method for high-dimensional data streams that is motivated by high density clustering. The method is capable of identifying clusters in arbitrary subspaces, estimating the number of clusters, and detecting changes in the data distribution which necessitate a revision of the model. The empirical evaluation of the proposed method on numerous real and simulated datasets shows that it is scalable in dimension and number of clusters, is robust to noisy and irrelevant features, and is capable of handling a variety of types of non-stationarity.


Clustering Data stream High dimensionality Population drift Modality testing 



David Hofmeyr gratefully acknowledges funding from both the Engineering and Physical Sciences Research Council (EPSRC) and the Oppenheimer Memorial Trust.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • David P. Hofmeyr
    • 1
    Email author
  • Nicos G. Pavlidis
    • 2
  • Idris A. Eckley
    • 1
  1. 1.Department of Mathematics and StatisticsLancaster UniversityLancasterUK
  2. 2.Department of Management ScienceLancaster UniversityLancasterUK

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