PSC - Predictive Subspace Clustering
The PSC algorithm performs clustering of high-dimensional data. The assumption is that, within each cluster, the data can be approximated well by a linear subspace estimated by means of a principal component analysis. PSC then partitions the data into clusters while simultaneously estimating cluster-wise PCA parameters. The algorithm minimises an objective function that depends upon a new measure of influence for PCA models. A penalised version of the algorithm is able to carry out simultaneous subspace clustering and variable selection.
McWilliams B. and Montana G. (2013) Subspace clustering of high-dimensional data: a predictive approach. Data Mining and Knowledge Discovery