# cluster analysis

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Related to cluster analysis: factor analysis, Discriminant analysis

## cluster analysis

[′kləs·tər ə′nal·ə·səs]
(statistics)
A general approach to multivariate problems whose aim is to determine whether the individuals fall into groups or clusters.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.

## cluster analysis

a technique used to identify groups of objects or people that can be shown to be relatively distinct within a data set. The characteristics of those people within each cluster can then be explored. In market research, for example, cluster analysis has been used to identify groups of people for whom different marketing approaches would be appropriate.

There is a rich variety of clustering methods available. A common method is hierarchical clustering which can work either from ‘bottom up’ or from ‘top down’. In ‘agglomerative hierarchical clustering’ (i.e. bottom up), the process begins with as many ‘clusters’ as cases. Using a mathematical criterion such as the standardized Euclidean distance, objects or people are successively joined together into clusters. In ‘divisive hierarchical clustering’ (i.e. top down), the process starts with one single cluster containing all cases, which is then broken down into smaller clusters.

There are many practical problems involved in the use of cluster analysis. The selection of variables to be included in the analysis, the choice of distance measure and the criteria for combining cases into clusters are all crucial. Because the selected clustering method can itself impose a certain amount of structure on the data, it is possible for spurious clusters to be obtained. In general, several different methods should be used. (See Anderberg, 1973, and Everitt, 1974, for full discussions of methods.)

Collins Dictionary of Sociology, 3rd ed. © HarperCollins Publishers 2000
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The two-step cluster analysis revealed three clusters, with their characteristics displayed in Table 3.
Functional Measurement methodology allows, however, to perform cluster analysis during different phases of the process, such as valuation, integration and response, thus generating new possibilities for interpreting consumers' judgments.
Geneticdiversity in soybean genotypes under drought stress condition using factor analysis and cluster analysis. World Appl.
In this study, we demonstrated that whole-genome cluster analysis of S.
The cluster analysis was done using statistical analysis software and on the basis of the k-means algorithm.
Table 4 shows the variance accounted by the first four PCs computed from the transmittance value of characteristic peaks used in cluster analysis. The first three PCs summarize more variation in the data than any other PCs, accounting for more than 98.58% of the data variance.
In this section, we apply our proposed significance analysis and cluster analysis method to a real-life gene expression dataset which is collected from the alpha-synchronized experiment [2].
Using PLS longitudinal dimension reduction, it can fully take into account the level of correlation between feature variables and the dependent variables and can solve the small sample problems caused by cluster analysis. Through cluster analysis, similar samples are classified as one close subclass and easy to train more precise neural network model.
Cluster analysis was conducted to separate corn varieties into several subgroups with respect to composition analysis and properties of corn flour to produce properties uniform varieties.
During the cluster analysis of spatial data, if the interaction between the data is negligible, the accuracy of the clustering results will have a very big impact.
In the general form of cluster analysis, each object in dataset will be assigned ultimately to a certain category.
Principal Component Analysis (PCA), Cluster Analysis (CA) and Factor Analysis (FA) are amongst the most prominent techniques used for urban planning (Nosoohi I & Hamdani A N, 2011).

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