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TwoStep Cluster Analysis Plots in Java Develop barcode 39 in Java TwoStep Cluster Analysis Plots

TwoStep Cluster Analysis Plots use jdk uss code 39 development toattach code 3/9 in java Code 128 Code Set C Figure 32-3 TwoStep Cluster Analysis Plots dialog box 478 32 . Within cluster percentage ch Java 3 of 9 art. Displays charts showing the within-cluster variation of each variable. For each categorical variable, a clustered bar chart is produced, showing the category frequency by cluster ID.

For each continuous variable, an error bar chart is produced, showing error bars by cluster ID. Cluster pie chart. Displays a pie chart showing the percentage and counts of.

observations within each cluster. Variable Importance Plot. Displays several different charts showing the importance of each variable within each cluster. The output is sorted by the importance rank of each variable. Rank Variables. This option determines whether plots will be created for each cluster (By variable) or for each variable (By cluster).. Importance Measure. This option allows you to select which measure of variable importance to plot. Chi-squa re or t-test of significance reports a Pearson chi-square statistic as the importance of a categorical variable and a t statistic as the importance of a continuous variable. Significance reports one minus the p value for the test of equality of means for a continuous variable and the expected frequency with the overall dataset for a categorical variable.

. Confidence level. This option allows you to set the confidence level for the test of equality of a variable s dis tribution within a cluster versus the variable s overall distribution. Specify a number less than 100 and greater than or equal to 50. The value of the confidence level is shown as a vertical line in the variable importance plots, if the plots are created by variable or if the significance measure is plotted.

. Omit insignificant variables. Variables that are not significant at the specified confidence level are not displayed in the variable importance plots. 479 TwoStep Cluster Analysis TwoStep Cluster Analysis Output Figure 32-4 TwoStep Cluster Analysis Output dialog box Statistics. This group provides options for displaying tables of the clustering results. The descriptive statistics a bar code 39 for Java nd cluster frequencies are produced for the final cluster model, while the information criterion table displays results for a range of cluster solutions.. Descriptives by cluster. Displays two tables that describe the variables in each cluster. In one table, means 3 of 9 barcode for Java and standard deviations are reported for continuous variables by cluster. The other table reports frequencies of categorical variables by cluster.

. Cluster frequencies. Displays a table that reports the number of observations in each cluster. Information criterion (AIC or BIC). Displays a table containing the values of the AIC or BIC, depending on the cri barcode 39 for Java terion chosen in the main dialog box, for different numbers of clusters. This table is provided only when the number of clusters is being determined automatically. If the number of clusters is fixed, this setting is ignored, and the table is not provided.

. 480 32 . Active dataset. This group a jvm barcode code39 llows you to save variables to the active dataset. Create cluster membership variable.

This variable contains a cluster identification. number for each case. The na me of this variable is tsc_n, where n is a positive integer indicating the ordinal of the active dataset save operation completed by this procedure in a given session..

XML Files. The final cluster jboss Code 3/9 model and CF tree are two types of output files that can be exported in XML format. Export final model.

The final cluster model is exported to the specified file in XML. (PMML) format. SmartScore an d SPSS Server (a separate product) can use this model file to apply the model information to other data files for scoring purposes..

Export CF tree. This option allows you to save the current state of the cluster tree and update it later using newer data. Hierarchical Cluster Analysis This procedure attempts to i dentify relatively homogeneous groups of cases (or variables) based on selected characteristics, using an algorithm that starts with each case (or variable) in a separate cluster and combines clusters until only one is left. You can analyze raw variables or you can choose from a variety of standardizing transformations. Distance or similarity measures are generated by the Proximities procedure.

Statistics are displayed at each stage to help you select the best solution.. Example. Are there identifiable groups of television shows that attract similar audiences within each group With hierarchical cluster analysis, you could cluster television shows (cases) into homogeneous groups based on viewer characteristics. This can be used to identify segments for marketing. Or you can cluster cities (cases) into homogeneous groups so that comparable cities can be selected to test various marketing strategies.

. Statistics. Agglomeration sc Code-39 for Java hedule, distance (or similarity) matrix, and cluster membership for a single solution or a range of solutions. Plots: dendrograms and icicle plots.

Data. The variables can be quantitative, binary, or count data. Scaling of variables is.

an important issue differenc es in scaling may affect your cluster solution(s). If your variables have large differences in scaling (for example, one variable is measured in dollars and the other is measured in years), you should consider standardizing them (this can be done automatically by the Hierarchical Cluster Analysis procedure)..

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