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.
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Received 30 August 2000 / Revised 30 January 2001 / Accepted in revised form 16 May 2001
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Kargupta, H., Huang, W., Sivakumar, K. et al. Distributed Clustering Using Collective Principal Component Analysis. Knowledge and Information Systems 3, 422–448 (2001). https://doi.org/10.1007/PL00011677
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DOI: https://doi.org/10.1007/PL00011677