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Clustering high-dimensional data

Known as: Subspace clustering 
Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions. Such high… 
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Papers overview

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2015
2015
Rank minimization problem can be boiled down to either Nuclear Norm Minimization (NNM) or Weighted NNM (WNNM) problem. The… 
2015
2015
Subspace clustering is the problem of clustering data points into a union of low-dimensional linear or affine subspaces. It is… 
2012
2012
We propose a clustering framework that supports clustering of datasets with mixed attribute type (numerical, categorical), while… 
2012
2012
The Emergency Department (ED) provides the first line of care for anyone seeking treatment for an urgent problem caused by an… 
2009
2009
Clustering is an established data mining technique for grouping objects based on similarity. For sensor networks one aims at… 
2007
2007
Data streams are often locally correlated, with a subset of streams exhibiting coherent patterns over a subset of time points… 
2007
2007
Abstract : Geometric harmonics provides a framework for taking data in high-dimensional measurement spaces and embedding them in… 
2005
2005
In high dimensional data analysis, finding non-Gaussian components is an important preprocessing step for efficient information… 
2004
2004
A shelf rail afixed to the front of gondola shelves and provided with slidably supported spring clips, said spring clips slidably… 
1998
1998
Contents Introduction 1 1 Unsupervised classiication of high-dimensional data 4 Introduction Unsupervised classiication is a…