cluster

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Related to Cluster analysis: factor analysis, Discriminant analysis
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This system employs K-means in cluster analysis and divides the datasets into different groups depending on certain parameters chosen from the outcome of the ontology and categorizing steps.
An initial two-step cluster analysis was performed entering all the technical test parameters (amplitude and frequency domains) just defined.
The retrospective part of the study serves as a proof of principle and clearly demonstrates increased resolution of whole-genome cluster analysis for typing of common PFGE pattern types and subsequent outbreak detection.
Spatial cluster analysis is not only an important part of spatial data mining, but also widely used in spatial data mining and in-depth study of one of the elements.
Beyers notes that he is unaware of others using cluster analysis to study state industry structure and unemployment trends and asserts that cluster analysis could be useful in future research into the relationship between industry structure and unemployment.
The segmentation of customers is a standard application of cluster analysis which allows segments to be formed that are based on data that are less dependent on subjectivity (Mooi & Sarstedt, 2011).
Most commonly two groups of cluster analysis methods are distinguished: a hierarchical cluster analysis and non-hierarchical cluster analysis.
It was observed from the cluster analysis in the present study that distinct new clusters are created with most impact on the rainfall data.
Cluster analysis is a multivariate procedure that simultaneously considers all classification variables to arrange a sample of entities into distinct groups according to shared characteristics (Stefos, Lavallee, and Holden 1992).
The researchers determined beverage consumption patterns among Canadian children aged two years using cluster analysis where sociodemographics, ethnicity, household income, and food security were significantly different across the clusters.