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… (More)
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2012
2012
High dimensional datasets usually present several dimensions which are irrelevant for certain clusters while they are relevant to… (More)
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2010
2010
Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining… (More)
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Highly Cited
2007
Highly Cited
2007
Clustering in high-dimensional spaces is a difficult problem which is recurrent in many domains, for example in image analysis… (More)
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2006
2006
In fuzzy clustering algorithms each object has a fuzzy membership associated with each cluster indicating the degree of… (More)
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Highly Cited
2005
Highly Cited
2005
Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in… (More)
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2004
2004
In high-dimensional feature spaces traditional clustering algorithms tend to break down in terms of efficiency and quality… (More)
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Highly Cited
2004
Highly Cited
2004
Clustering suffers from the curse of dimensionality, and similarity functions that use all input features with equal relevance… (More)
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Highly Cited
2004
Highly Cited
2004
Several application domains such as molecular biology and geography produce a tremendous amount of data which can no longer be… (More)
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Highly Cited
2000
Highly Cited
2000
The coming century is surely the century of data. A combination of blind faith and serious purpose makes our society invest… (More)
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Highly Cited
1998
Highly Cited
1998
Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in… (More)
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