![]() ![]() For this dataset, we will demonstrate an even simpler approach. This can be accomplished by linearly scaling the data of each attribute between -1 and 1, or by replacing attribute values with the number of standard deviations each have from the attribute mean value. One of the most common is to normalize the results in some fashion so that the differences in scale of the numerical attributes do not dominate the Euclidean distance measure. What instance percentage is incorrectly clustered? Visualization In k-means Clustering, there are a number of ways one can often improve results. Note the cluster centroids in the Clusterer output pane. ![]() Press Start to begin k-means Clustering and evaluation. ![]() Under Cluster Mode, select the radio button Classes to cluster evaluation which should be follows by (nom) class by default. 3 Click the SimpleKMeans command box to the right of the Choose button, change the numclusters attribute to 3, and click the OK button. ![]()
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