Skip to search formSkip to main contentSkip to account menu

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… 
Wikipedia (opens in a new tab)

Related topics

Related topics

13 relations
Association rule learningBiclusteringBioinformaticsCorrelation clustering

Broader (1)

Cluster analysis

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
Large-scale Subspace Clustering by Fast Regression Coding
Large-Scale Subspace Clustering (LSSC) is an interesting and important problem in big data era. However, most existing methods (i… 
2014
2014
Finding the Optimal Subspace for Clustering
The ability to simplify and categorize things is one of the most important elements of human thought, understanding, and learning… 
Review
2013
Review
2013
Clustering for High Dimensional Data: Density based Subspace Clustering Algorithms
clusters in high dimensional data is a challenging task as the high dimensional data comprises hundreds of attributes. Subspace… 
2012
2012
Improved subspace clustering via exploitation of spatial constraints
We present a novel approach to improving subspace clustering by exploiting the spatial constraints. The new method encourages the… 
Highly Cited
2012
Highly Cited
2012
Density-Based Projected Clustering of Data Streams
In this paper, we have proposed, developed and experimentally validated our novel subspace data stream clustering, termed… 
2010
2010
An Improved Initialization Method for Clustering High-Dimensional Data
Searching initial centers in high dimensional space is an interesting and important problem which is relevant for the wide… 
2010
2010
High Performance Dimension Reduction and Visualization for Large High-Dimensional Data Analysis
Large high dimension datasets are of growing importance in many fields and it is important to be able to visualize them for… 
2009
2009
An Initialization Method for Clustering High-Dimensional Data
In iterative refinement clustering algorithms, such as the various types of K-Means algorithms, the clustering results are very… 
2007
2007
Diffusion Maps and Geometric Harmonics for Automatic Target Recognition (ATR). Volume 2. Appendices
Abstract : Geometric harmonics provides a framework for taking data in high-dimensional measurement spaces and embedding them in… 
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… 
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)