Modeling and Multivariate Methods - SAS

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466 Clustering Data Chapter 18 Hierarchical Clustering Hierarchical Cluster Options Hierarchical clustering has the popup menu with the following commands. Color Clusters assigns colors to the rows of the data table corresponding to the cluster the row belongs to. Also colors the dendogram according to the clusters. The colors automatically update if you change the number of clusters. Mark Clusters assigns markers to the rows of the data table corresponding to the cluster the row belongs to. The markers automatically update if you change the number of clusters. Number of Clusters that number. prompts you to enter a number of clusters and positions the dendrogram slider to Cluster Criterion Show Dendogram gives the Cubic Clustering Criterion for range of number of clusters. shows or hides the dendogram. Dendrogram Scale contains options for scaling the dendrogram. Even Spacing shows the distance between each join point as equal. Geometric Spacing is useful when there are many clusters and you want the clusters near the top of the tree to be more visible than those at the bottom. (This option is the default for more than 256 rows). Distance Scale shows the actual joining distance between each join point, and is the same scale used on the plot produced by the Distance Graph command. Distance Graph shows or hides the scree plot at the bottom of the histogram. Show NCluster Handle number of clusters. shows or hides the handles on the dendogram used to manually change the Zoom to Selected Rows is used to zoom the dendogram to a particular cluster after selecting the cluster on the dendogram. Alternatively, you can double-click on a cluster to zoom in on it. Release Zoom returns the dendogram to original view after zooming. Pivot on Selected Cluster reverses the order of the two sub-clusters of the currently selected cluster. Color Map is an option to add a color map showing the values of all the data colored across its value range. There are several color theme choices in a submenu. Another term for this feature is heat map. Two way clustering adds clustering by column. A color map is automatically added with the column dendrogram at its base. The columns must be measured on the same scale. Positioning provides options for changing the positions of dendograms and labels. Legend shows or hides a legend for the colors used in a color map. This option is available only if a color map is enabled. More Color Map Columns adds a color map for specified columns. Save Clusters creates a data table column containing the cluster number. Save Display Order dendogram. creates a data table column containing the order the row is presented in the
Chapter 18 Clustering Data 467 Hierarchical Clustering Save Cluster Hierarchy saves information needed if you are going to do a custom dendrogram with scripting. For each clustering, it outputs three rows, the joiner, the leader, and the result, with the cluster centers, size, and other information. Save Distance Matrix makes a new data table containing the distances between the observations. Parallel Coord Plots creates a parallel coordinate plot for each cluster. For details about the plots, see Basic Analysis and Graphing. Script contains options that are available to all platforms. See Using JMP. Technical Details for Hierarchical Clustering The following description of hierarchical clustering methods gives distance formulas that use the following notation. Lowercase symbols generally pertain to observations and uppercase symbols to clusters. n is the number of observations v is the number of variables x i is the ith observation C K is the Kth cluster, subset of {1, 2,..., n} N K is the number of observations in C K x is the sample mean vector x K x x) is the mean vector for cluster C K is the square root of the sum of the squares of the elements of x (the Euclidean length of the vector d(x i , x j ) is x 2 Average Linkage In average linkage, the distance between two clusters is the average distance between pairs of observations, or one in each cluster. Average linkage tends to join clusters with small variances and is slightly biased toward producing clusters with the same variance. See Sokal and Michener (1958). Distance for the average linkage cluster method is D KL = d( x i , x j ) ------------------ i ∈ C K j ∈ C L N K N L Centroid Method In the centroid method, the distance between two clusters is defined as the squared Euclidean distance between their means. The centroid method is more robust to outliers than most other hierarchical methods but in other respects might not perform as well as Ward’s method or average linkage. See Milligan (1980). Distance for the centroid method of clustering is 2 D KL = x K – x L
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640 References Becker, R.A. and Cle
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690 Index Birth Death Subset.jmp 46
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696 Index Customizing 267 Nonlinear
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698 Index reliability analysis 453-
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700 Index Stable 363 Stable Inverti
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Modeling and Multivariate Methods - SAS

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