Knowledge and Information Systems

, Volume 3, Issue 4, pp 422–448 | Cite as

Distributed Clustering Using Collective Principal Component Analysis

  • Hillol Kargupta
  • Weiyun Huang
  • Krishnamoorthy Sivakumar
  • Erik Johnson
Regular Paper

Abstract.

This paper considers distributed clustering of high-dimensional heterogeneous data using a distributed principal component analysis (PCA) technique called the collective PCA. It presents the collective PCA technique, which can be used independent of the clustering application. It shows a way to integrate the Collective PCA with a given off-the-shelf clustering algorithm in order to develop a distributed clustering technique. It also presents experimental results using different test data sets including an application for web mining.

Keywords: Collective principal component analysis; Distributed clustering 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag London Limited 2001

Authors and Affiliations

  • Hillol Kargupta
    • 1
  • Weiyun Huang
    • 2
  • Krishnamoorthy Sivakumar
    • 2
  • Erik Johnson
    • 2
  1. 1.Computer Science and Electrical Engineering Department, University of Maryland, Baltimore, Maryland, USAUS
  2. 2.School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, USAUS

Personalised recommendations