Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions. Such high-dimensional...

well matched to its own cluster and poorly matched to neighboring clusters. If most objects have a high value, then the clustering configuration is appropriate...

to Cluster analysis. Automatic clustering algorithms Balanced clustering Clustering high-dimensional data Conceptual clustering Consensus clustering Constrained...

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the...

Clustering is the problem of partitioning data points into groups based on their similarity. Correlation clustering provides a method for clustering a...

between data points with indices i {\displaystyle i} and j {\displaystyle j} . The general approach to spectral clustering is to use a standard clustering method...

mixture modeling. They both use cluster centers to model the data; however, k-means clustering tends to find clusters of comparable spatial extent, while...

The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces that do not occur in low-dimensional...

issue from the process of actually solving the clustering problem. For a certain class of clustering algorithms (in particular k-means, k-medoids and...

In statistical theory, the field of high-dimensional statistics studies data whose dimension is larger (relative to the number of datapoints) than typically...