Distributed Clustering Using Collective Principal Component Analysis

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.