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

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...

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...

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

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

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...

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...

an algorithm for clustering high-dimensional data by Karin Kailing, Hans-Peter Kriegel and Peer Kröger. It is a subspace clustering algorithm that builds...

hierarchy of clusters. Strategies for hierarchical clustering generally fall into two categories: Agglomerative: Agglomerative clustering, often referred...

In computer science, data stream clustering refers to the process of grouping data points that arrive in a continuous, rapid, and potentially unbounded...

Biclustering, block clustering, Co-clustering or two-mode clustering is a data mining technique which allows simultaneous clustering of the rows and columns...

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

statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map. It is based on Stochastic...

selection algorithm Cluster-weighted modeling Clustering high-dimensional data Clustering illusion CoBoosting Cobweb (clustering) Cognitive computer Cognitive...

probability, etc. Intrinsically, functional data are infinite dimensional. The high intrinsic dimensionality of these data brings challenges for theory as well...

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

low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data. For...

incrementally and dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the best quality clustering for a given set of resources...

useful for clustering. Different Gaussian model-based clustering methods have been developed with an eye to handling high-dimensional data. These include...

K-means algorithm or the hierarchical clustering algorithm. It is intended to speed up clustering operations on large data sets, where using another algorithm...

(Clustering Using REpresentatives) is an efficient data clustering algorithm for large databases[citation needed]. Compared with K-means clustering it...

2022). "K Means Clustering on High Dimensional Data". Medium. "What are the main drawbacks of using k-means for high-dimensional data?".[self-published...

diagram Rate-distortion function Data clustering Centroidal Voronoi tessellation Image segmentation K-means clustering Autoencoder Deep Learning Part of...

procedure Cluster analysis Cluster randomised controlled trial Cluster sampling Cluster-weighted modeling Clustering high-dimensional data CMA-ES (Covariance...

Consensus clustering is a method of aggregating (potentially conflicting) results from multiple clustering algorithms. Also called cluster ensembles or...

result in a reduced vector. When the data vectors are high-dimensional it is computationally infeasible to use data analysis or pattern recognition algorithms...

as a way to reduce the dimensionality of high-dimensional data; high-dimensional input items can be reduced to low-dimensional versions while preserving...

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg...

Nonlinear dimensionality reduction, also known as manifold learning, is any of various related techniques that aim to project high-dimensional data, potentially...