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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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Related topics

Related topics

13 relations
Association rule learningBiclusteringBioinformaticsCorrelation clustering

Broader (1)

Cluster analysis

Papers overview

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Highly Cited
2019
Highly Cited
2019
Fast Adaptive K-Means Subspace Clustering for High-Dimensional Data
In many real-world applications, data are represented by high-dimensional features. Despite the simplicity, existing K-means… 
Highly Cited
2016
Highly Cited
2016
Spectral–Spatial Sparse Subspace Clustering for Hyperspectral Remote Sensing Images
Clustering for hyperspectral images (HSIs) is a very challenging task due to its inherent complexity. In this paper, we propose a… 
Highly Cited
2013
Highly Cited
2013
Robust Subspace Clustering
Subspace clustering refers to the task of nding a multi-subspace representation that best ts a collection of points taken from a… 
2012
2012
Dimension Selective Self-Organizing Maps for clustering high dimensional data
High dimensional datasets usually present several dimensions which are irrelevant for certain clusters while they are relevant to… 
Highly Cited
2009
Highly Cited
2009
Particle swarm optimizer for variable weighting in clustering high-dimensional data
In this paper, we present a particle swarm optimizer (PSO) to solve the variable weighting problem in projected clustering of… 
Highly Cited
2006
Highly Cited
2006
A Fuzzy Subspace Algorithm for Clustering High Dimensional Data
In fuzzy clustering algorithms each object has a fuzzy membership associated with each cluster indicating the degree of… 
2005
2005
Clustering High-Dimensional Data Using Growing SOM
The self-organizing map (SOM) is a very popular unsupervised neural-network model for analyzing of high-dimensional input data as… 
Highly Cited
2004
Highly Cited
2004
Subspace Clustering of High Dimensional Data
Clustering suffers from the curse of dimensionality, and similarity functions that use all input features with equal relevance… 
Highly Cited
2004
Highly Cited
2004
Subspace clustering for high dimensional categorical data
Data clustering has been discussed extensively, but almost all known conventional clustering algorithms tend to break down in… 
Highly Cited
1999
Highly Cited
1999
Entropy-based subspace clustering for mining numerical data
Mining numerical data is a relatively difficult problem in data mining. Clustering is one of the techniques. We consider a… 
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