Shows a plot of the points and clusters in the first two principal components of the data, along with a legend identifying the cluster colors. Circles are drawn around the cluster centers and the size of the circles is proportional to the count inside the cluster. The shaded area is the density contour around the mean. By default, this area indicates where 90% of the observations in that cluster would fall (Mardia et al. 1980). Use the list below the plot to change the plot axes to other principal components. Alternatively, use the arrow button to cycle through all possible axes combinations. An option to save the cluster colors to the data table is also located below the plot. For details, see Save Colors to Table. The eigenvalues are shown in decreasing order.
Shows a three-dimensional biplot of the data. Available only when there are three or more variables.
Creates a parallel coordinate plot for each cluster. The plot report has options for showing and hiding the data and means. For details, see “Parallel Plots” in the Essential Graphing book.
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The Cluster column contains the number of the cluster to which the given row is assigned.
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(Not available for Self Organizing Maps.) The Distance column contains the squared Euclidean distance between the given observation and its cluster mean. For each variable, the difference between the observation’s value and the cluster mean on that variable is divided by the overall standard deviation for the variable. These scaled differences are squared and summed across the variables.
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(Not available for Self Organizing Maps.) Saves a Distance column to the data table. This column is the same as the Distance column obtained from the Save Clusters option.
(Not available for Self Organizing Maps.) Saves k columns containing the squared Euclidean distances to each cluster center.
(Not available for Self Organizing Maps.) Saves k columns containing the formulas for the squared Euclidean distances to each cluster center.