Look up clustering in Wiktionary, the free dictionary. Clustering can refer to the following: In computing: Computer cluster, the technique of linking...

k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which...

statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter...

clusters. Strategies for hierarchical clustering generally fall into two categories: Agglomerative: Agglomerative clustering, often referred to as a "bottom-up"...

have a low or negative value, then the clustering configuration may have too many or too few clusters. A clustering with an average silhouette width of over...

{\displaystyle j} . The general approach to spectral clustering is to use a standard clustering method (there are many such methods, k-means is discussed...

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

computer science, constrained clustering is a class of semi-supervised learning algorithms. Typically, constrained clustering incorporates either a set of...

In finance, volatility clustering refers to the observation, first noted by Mandelbrot (1963), that "large changes tend to be followed by large changes...

assembled to reconstruct the original mRNA. Some clustering algorithms use single-linkage clustering, constructing a transitive closure of sequences with...

of the clustering in the network, whereas the local gives an indication of the extent of "clustering" of a single node. The local clustering coefficient...

clustering is the gathering of various populations based on ethnicity, economics, or religion. In countries that hold equality important, clustering occurs...

Automatic clustering algorithms are algorithms that can perform clustering without prior knowledge of data sets. In contrast with other cluster analysis...

arising in the US Marine Corps Clusters School of Digital Arts, an animation and visual effects training school Clustering (disambiguation) This disambiguation...

Document clustering (or text clustering) is the application of cluster analysis to textual documents. It has applications in automatic document organization...

clustering (also referred to as soft clustering or soft k-means) is a form of clustering in which each data point can belong to more than one cluster...

search engine results (SERP). Keyword clustering is a fully automated process performed by keyword clustering tools. The term and the first principles...

iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large...

basis for clustering, and ways to choose the number of clusters, to choose the best clustering model, to assess the uncertainty of the clustering, and to...

Balanced clustering is a special case of clustering where, in the strictest sense, cluster sizes are constrained to ⌊ n k ⌋ {\displaystyle \lfloor {n...

distinguished from ordinary data clustering by generating a concept description for each generated class. Most conceptual clustering methods are capable of generating...

single-linkage clustering is one of several methods of hierarchical clustering. It is based on grouping clusters in bottom-up fashion (agglomerative clustering), at...

Microgrid clustering is connecting and controlling multiple microgrids within a certain range of distance (e.g. neighborhood) to either gain economic benefits...

Quantum Clustering (QC) is a class of data-clustering algorithms that use conceptual and mathematical tools from quantum mechanics. QC belongs to the...

The clustering illusion is the tendency to erroneously consider the inevitable "streaks" or "clusters" arising in small samples from random distributions...

are orchestrated by "clustering middleware", a software layer that sits atop the nodes and allows the users to treat the cluster as by and large one cohesive...

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

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

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

Complete-linkage clustering is one of several methods of agglomerative hierarchical clustering. At the beginning of the process, each element is in a cluster of its...