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The resulting maps display the individual statements in two-dimensional space with more similar statements located closer to each other, and grouped into clusters that partition the space on the map. The Concept System software also creates other maps that show the statements in each cluster rated on one or more scales, and absolute or relative ...
Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions.Such high-dimensional spaces of data are often encountered in areas such as medicine, where DNA microarray technology can produce many measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions ...
Self-organizing maps, like most artificial neural networks, operate in two modes: training and mapping. First, training uses an input data set (the "input space") to generate a lower-dimensional representation of the input data (the "map space"). Second, mapping classifies additional input data using the generated map.
Finding clusters in the network (e.g. grouping Facebook friends into different clusters). Discovering bridges (information brokers or boundary spanners) between clusters in the network; Determining the most influential nodes in the network (e.g. A company wants to target a small group of people on Twitter for a marketing campaign).
The common practice of factor rotation has obscured the similarity between different studies with different orientations of the axes on the cultural maps. The unrotated solution has the strongest factor or dimension corresponding to a line from the lower left to the upper right of Inglehart and Welzel's map, combining the two dimensions.
If a Gaussian mixture is fitted to such data, a strongly non-Gaussian cluster will often be represented by several mixture components rather than a single one. In that case, cluster merging can be used to find a better clustering. [20] A different approach is to use mixtures of complex component densities to represent non-Gaussian clusters. [21 ...
A bivariate map or multivariate map is a type of thematic map that displays two or more variables on a single map by combining different sets of symbols. [1] Each of the variables is represented using a standard thematic map technique, such as choropleth, cartogram, or proportional symbols. They may be the same type or different types, and they ...
A cluster in general is a group or bunch of several discrete items that are close to each other. The cluster diagram figures a cluster, such as a network diagram figures a network, a flow diagram a process or movement of objects, and a tree diagram an abstract tree. But all these diagrams can be considered interconnected: A network diagram can ...