When the data is binary, the remaining two options, Jaccard's coefficients and Matching coefficients, are enabled. The Hierarchical Clustering method uses the Euclidean distance as the similarity measure for raw numeric data. Under Similarity Measure, Euclidean distance is selected by default. Without normalization, the variable with the largest scale will dominate the measure. Normalizing the data is important to ensure that the distance measure accords equal weight to each variable. Select Normalize input data to normalize the data by subtracting the variable's mean from each observation, and dividing by the standard deviation. Select any cell in the data set, then on the XLMiner ribbon, from the Data Analysis tab, select Cluster - Hierarchical Clustering to open the Hierarchical Clustering dialog.įrom the Variables in Input Data list, select variables x1 through x8, then click > to move the selected variables to the Selected Variables list.Ĭlick Next to advance to Step 2 of 3 dialog. X5: Peak KWH demand growth from 1974 to 1975 X1: Fixed - charge covering ration (income/debt) On the XLMiner ribbon, from the Applying Your Model tab, select Help - Examples, thenselect Forecasting/Data Mining Examples, and open the Utilities.xlsx example data set.įollowing is an explanation of the variables. After the variables are standardized, the distance can be computed between clusters using the Euclidean metric. A popular method for normalizing continuous variables is to divide each variable by its standard deviation. Before using a clustering technique, the data must be normalized or standardized. It would save a considerable amount of time and effort by clustering similar types of utilities, building a detailed cost model for just one typical utility in each cluster, then scaling up from these models to estimate results for all utilities.Įach record includes eight observations. To perform the requisite analysis, economists would be required to build a detailed cost model of the various utilities.
#Hierarchical database example how to#
This example illustrates how to use XLMiner to perform a cluster analysis using hierarchical clustering.Īn example where clustering would be useful is a study to predict the cost impact of deregulation. The utilities.xlsx example data set (shown below) holds corporate data on 22 U.S.
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