Dynamic Subspace Clustering for Very Large High-Dimensional Databases

  • P. Deepa Shenoy
  • K. G. Srinivasa
  • M. P. Mithun
  • K. R. Venugopal
  • L. M. Patnaik
Conference paper

DOI: 10.1007/978-3-540-45080-1_117

Volume 2690 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Shenoy P.D., Srinivasa K.G., Mithun M.P., Venugopal K.R., Patnaik L.M. (2003) Dynamic Subspace Clustering for Very Large High-Dimensional Databases. In: Liu J., Cheung Y., Yin H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg

Abstract

Emerging high-dimensional data mining applications needs to find interesting clusters embeded in arbitrarily aligned subspaces of lower dimensionality. It is difficult to cluster high-dimensional data objects, when they are sparse and skewed. Updations are quite common in dynamic databases and they are usually processed in batch mode. In very large dynamic databases, it is necessary to perform incremental cluster analysis only to the updations. We present a incremental clustering algorithm for subspace clustering in very high dimensions, which handles both insertion and deletions of datapoints to the backend databases.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • P. Deepa Shenoy
    • 1
  • K. G. Srinivasa
    • 1
  • M. P. Mithun
    • 1
  • K. R. Venugopal
    • 1
  • L. M. Patnaik
    • 2
  1. 1.University Visvesvaraya College of EngineeringBangalore
  2. 2.Microprocessor Application LaboratoryIndian Institute of ScienceBangaloreIndia