cluster analysis

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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.

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.)

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To understand these interactions better, we used data from the West Coast Groundfish Observer Program in a series of cluster analyses to evaluate 3 questions: 1) Are there identifiable associations between species caught in the bottom trawl fishery; 2) Do species that are undergoing population rebuilding toward target biomass levels ("rebuilding species") cluster with targeted species in a consistent way; 3) Are the relationships between rebuilding bycatch species and target species more resolved at particular spatial scales or are relationships spatially consistent across the whole data set?
Multivariate and cluster analyses appropriate to different applications, and the conditions under which they apply, are discussed in detail, with several chapters emphasizing interpretation.
Four cluster analyses were performed to classify hospitals into three peer groups by removing the variable that most weakly affected previous clustering.
The authors investigated 109 MM cases recorded in the Cancer Registry of New Caledonia and performed spatial, temporal, and space-time cluster analyses.
In this study, we conducted cluster analyses using only motivational continuum scores, and subsequently examined how these motivational profiles are associated with outcomes of engagement in physical education.
Before any cluster analyses were conducted, general frequencies on demographic variables were calculated for the 26 couples (52 individuals) who participated in the experiments.
2001), initial specific gravities and heartwood ratios were classified by hierarchical cluster analyses and those clusters showed relationships to drying characteristics.
Preliminary, parallel cluster analyses were performed for participants in each racial, gender and school grade combination; these analyses yielded the same basic cluster structure for each subsample.
Similarity coefficients were calculated by using the Dice algorithm, and cluster analyses were performed by the neighbor-joining algorithms by using the "Fuzzy Logic" and "Area Sensitive" option of the GelcomparII program.