Ensembles based on random projections to improve the accuracy of clustering algorithms
Alberto Bertoni and Giorgio Valentini
In: Neural Nets Lecture Notes in Computer Science , 3931 . (2006) Springer , Berlin , pp. 31-37.

Abstract

We present an algorithmic scheme for unsupervised cluster ensembles, based on randomized projections between metric spaces, by which a substantial dimensionality reduction is obtained. Multiple clusterings are performed on random subspaces, approximately preserving the distances between the projected data, and then they are combined using a pairwise similarity matrix; in this way the accuracy of each ``base" clustering is maintained, and the diversity between them is improved. The proposed approach is effective for clustering problems characterized by high dimensional data, as shown by our preliminary experimental results.

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