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Related to Cluster analysis: factor analysis, Discriminant analysis
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For the current cluster analysis, the resulting dendrogram and custom basic tables yielded 10 clusters, with only 4 clusters containing more than 1 couple, which were therefore considered to be common decision-making strategies (Figure 1 contains the dendrogram with the clusters from the custom basic tables highlighted).
The term cluster analysis actually encompasses a number of different classification algorithms, which organize observed data into meaningful structure.
Cluster analysis is a powerful tool in data mining.
These variables become the basis for the cluster analysis.
Cluster analysis uses various statistical algorithms to identify groups of similar records and label them according to the group to which they belong.
Keywords: Genetic diversity, Sugarcane, SSR markers, PAGE, Cluster analysis, Principal Coordinate Analysis.
Besides the two dominating methods, researchers in this field use cluster analysis methods.
At this moment, data mining technology will be used for other above the results of cluster analysis.
Existence of genetic variation permits adding genetically similar cows into groups using multivariate cluster analysis based on EBVs for milk yields on each test day or on components of the lactation curve.
Among the various multivariate techniques found in the literature, undoubtedly, the most used is the cluster analysis, as researchers generally seek the recognition of patterns, aiming to simplify and explain the behaviour of a forest and a group of plots or a plot through the measurement of various species.
A cluster analysis is applied in order to group countries with similar gender and labor market characteristics together.