- cluster dimensionality
- Математика: кластерная размерность
Универсальный англо-русский словарь. Академик.ру. 2011.
cluster dimensionality
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Cluster analysis (in marketing) — Cluster analysis is a class of statistical techniques that can be applied to data that exhibit “natural” groupings. Cluster analysis sorts through the raw data and groups them into clusters. A cluster is a group of relatively homogeneous cases or … Wikipedia
Cluster analysis — The result of a cluster analysis shown as the coloring of the squares into three clusters. Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more… … Wikipedia
Curse of dimensionality — The curse of dimensionality refers to various phenomena that arise when analyzing and organizing high dimensional spaces (often with hundreds or thousands of dimensions) that do not occur in low dimensional settings such as the physical space… … Wikipedia
Principal component analysis — PCA of a multivariate Gaussian distribution centered at (1,3) with a standard deviation of 3 in roughly the (0.878, 0.478) direction and of 1 in the orthogonal direction. The vectors shown are the eigenvectors of the covariance matrix scaled by… … Wikipedia
Clustering high-dimensional data — is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions. Such high dimensional data spaces are often encountered in areas such as medicine, where DNA microarray technology can produce a large number of… … Wikipedia
Nearest neighbor search — (NNS), also known as proximity search, similarity search or closest point search, is an optimization problem for finding closest points in metric spaces. The problem is: given a set S of points in a metric space M and a query point… … Wikipedia
Determining the number of clusters in a data set — Determining the number of clusters in a data set, a quantity often labeled k as in the k means algorithm, is a frequent problem in data clustering, and is a distinct issue from the process of actually solving the clustering problem. For a certain … Wikipedia
Granular computing — is an emerging computing paradigm of information processing. It concerns the processing of complex information entities called information granules, which arise in the process of data abstraction and derivation of knowledge from information.… … Wikipedia
Semantic mapping (statistics) — The semantic mapping (SM) is a dimensionality reduction method that extracts new features by clustering the original features in semantic clusters and combining features mapped in the same cluster to generate an extracted feature. Given a data… … Wikipedia
Dimension reduction — For dimensional reduction in physics, see Dimensional reduction. In machine learning, dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature… … Wikipedia
Coordination polymer — A coordination polymer is an inorganic or organometallic polymer structure containing metal cation centers linked by ligands, extending in an array. It can also be described as a polymer whose repeat units are coordination complexes. Similar… … Wikipedia
