k-Means Clustering Results

k-Means Clustering

Click the OK button in the Cluster Analysis: K-Means Clustering dialog box to display the k-Means Clustering Results dialog box (if the Batch processing and reporting check box is not selected on the Cluster Analysis: K-Means Clustering dialog box - Advanced tab). This dialog box contains two tabs: Quick and Advanced.

Summary box. The Summary box at the top of the dialog displays a general summary of the current analysis.

Copy button. Click the Copy button to copy either the selected text (if text has been selected) in the Summary box or all of the text (if no text has been selected) to the Clipboard. Note that the copied text retains formatting information (such as font, color, etc.).

Contract/Expand button. Click the Contract/Expand button to contract or expand the Summary box. When contracted, you can see only one line of the Summary box text and can scroll through the text using a scroll bar. Note that when contracted the text is scrolled so that the first non-blank line is at the top. When expanded (the default setting), the entire Summary box will be displayed in the k-Means Clustering Results dialog box.

Summary. Click the Summary button to display two spreadsheets:

  • A spreadsheet with the means for each cluster for each dimension;

  • A spreadsheet with the Euclidean distances (below the diagonal) and squared Euclidean distances (above the diagonal) between "cluster centers."

Specifically, this matrix shows the Euclidean distances between clusters, computed from the respective cluster means on the dimensions used for the classification. The distance between two objects or cluster centers i and j are computed as:

Di,j = Ö{S[(xi - xj )2 /ND]}

where the summation is over the ND dimensions in the current analysis.

Cancel. Click the Cancel button to return to the Cluster Analysis: K-Means Clustering dialog box.

Options. Click the Options button to display the Options menu.

See also, Differences in k-Means Algorithms in Generalized EM & k-Means Cluster Analysis vs. Cluster Analysis.