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Subspace clustering of highdimensional data: a predictive approach, Data Mining and Knowledge Discovery 28
, 2014
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Multiview predictive partitioning in high dimensions
 Statistical Analysis and Data Mining
"... Many modern data mining applications are concerned with the analysis of datasets in which the observations are described by paired highdimensional vectorial representations or “views”. Some typical examples can be found in web mining and genomics applications. In this article we present an algorith ..."
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Many modern data mining applications are concerned with the analysis of datasets in which the observations are described by paired highdimensional vectorial representations or “views”. Some typical examples can be found in web mining and genomics applications. In this article we present an algorithm for data clustering with multiple views, MultiView Predictive Partitioning (MVPP), which relies on a novel criterion of predictive similarity between data points. We assume that, within each cluster, the dependence between multivariate views can be modelled by using a twoblock partial least squares (TBPLS) regression model, which performs dimensionality reduction and is particularly suitable for highdimensional settings. The proposed MVPP algorithm partitions the data such that the withincluster predictive ability between views is maximised. The proposed objective function depends on a measure of predictive influence of points under the TBPLS model which has been derived as an extension of the PRESS statistic commonly used in ordinary least squares regression. Using simulated data, we compare the performance of MVPP to that of competing multiview clustering methods which rely upon geometric structures of points, but ignore the predictive relationship between the two views. Stateofart results are obtained on benchmark web mining datasets. 1
Projection Based Models for High Dimensional Data
, 2011
"... 2I certify that this thesis, and the research to which it refers, are the product of my own work, and that any ideas or quotations from the work of other people, published or otherwise, are fully acknowledged in accordance with the standard referencing practices of the discipline. Signed: ..."
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2I certify that this thesis, and the research to which it refers, are the product of my own work, and that any ideas or quotations from the work of other people, published or otherwise, are fully acknowledged in accordance with the standard referencing practices of the discipline. Signed: