IMSL C# Programmers Guide
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Charting 2D Types >> Dendrogram Chart |
Dendrogram Chart
A dendrogram chart is a graphical way to display results from hierarchical cluster analysis. This section describes the construction of a dendrogram chart.
Example
The data for this example is grouped into clusters using the Dissimilarities
and ClusterHierarchical
classes. A Dendrogram
node is then created as
a child of an axis node. The Dendrogram
constructor requires input values from
the ClusterHierarchical
object.
The SetLables
and SetLineColor
methods are used to customize the look
of the chart. Labels are provided in a String
array in the order of the input data
and sorted by the Dendrogram
object to match the output order. Clusters are
grouped by color based on the number of elements in the array passed to the
SetLineColor
method.
(Download Code)
using Imsl.Chart2D; using Imsl.Stat; using System.Drawing; public class SampleDendrogram : FrameChart { public SampleDendrogram() { /* 1998 test data from 17 school districts in Los Angeles County. The variables were: lep - Proportion of LEP students to total tested read - The Reading Scaled Score for 5th Grade math - The Math Scaled Score for 5th Grade lang - The Language Scaled Score for 5th Grade The districts were: lau - Los Angeles ccu - Culver City bhu - Beverly Hills ing - Inglewood com - Compton smm - Santa Monica Malibu bur - Burbank gln - Glendale pvu - Palos Verdes sgu - San Gabriel abc - Artesia, Bloomfield, and Carmenita pas - Pasadena lan - Lancaster plm - Palmdale tor - Torrance dow - Downey lbu - Long Beach input lep read math lang str3 district .38 626.5 601.3 605.3 lau .18 654.0 647.1 641.8 ccu .07 677.2 676.5 670.5 bhu .09 639.9 640.3 636.0 ing .19 614.7 617.3 606.2 com .12 670.2 666.0 659.3 smm .20 651.1 645.2 643.4 bur .41 645.4 645.8 644.8 gln .07 683.5 682.9 674.3 pvu .39 648.6 647.8 643.1 sgu .21 650.4 650.8 643.9 abc .24 637.0 636.9 626.5 pas .09 641.1 628.8 629.4 lan .12 638.0 627.7 628.6 plm .11 661.4 659.0 651.8 tor .22 646.4 646.2 647.0 dow .33 634.1 632.0 627.8 lbu */ double[,] data = { {.38, 626.5, 601.3, 605.3}, {.18, 654.0, 647.1, 641.8}, {.07, 677.2, 676.5, 670.5}, {.09, 639.9, 640.3, 636.0}, {.19, 614.7, 617.3, 606.2}, {.12, 670.2, 666.0, 659.3}, {.20, 651.1, 645.2, 643.4}, {.41, 645.4, 645.8, 644.8}, {.07, 683.5, 682.9, 674.3}, {.39, 648.6, 647.8, 643.1}, {.21, 650.4, 650.8, 643.9}, {.24, 637.0, 636.9, 626.5}, {.09, 641.1, 628.8, 629.4}, {.12, 638.0, 627.7, 628.6}, {.11, 661.4, 659.0, 651.8}, {.22, 646.4, 646.2, 647.0}, {.33, 634.1, 632.0, 627.8}}; string[] lab = {lau, ccu, bhu, ing, com, smm, bur, gln, pvu, sgu, abc, pas, lan, plm, tor, dor, lbu}; // 3rd arg in Dissimilarities gives different results for 0,1,2 Dissimilarities dist = new Dissimilarities(data, 0, 1, 1); ClusterHierarchical clink = new ClusterHierarchical(dist.DistanceMatrix, 4, 0); int nClusters = 4; int[] iclus = clink.GetClusterMembership(nClusters); int[] nclus = clink.GetObsPerCluster(nClusters); Chart chart = this.Chart; AxisXY axis = new AxisXY(chart); // use either method below to create the chart Dendrogram dc = new Dendrogram(axis, clink, Data.DENDROGRAM_TYPE_HORIZONTAL); /* new Dendrogram(axis, clink.getClusterLevel(), clink.getClusterLeftSons(), clink.getClusterRightSons(), Data.DENDROGRAM_TYPE_HORIZONTAL); */ dc.SetLabels(lab); dc.SetLineColors( new Color[]{Color.Blue, Color.Green, Color.Red, Color.Orange}); } public static void Main(string[] argv) { System.Windows.Forms.Application.Run(new SampleDendrogram()); } }
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