k-Means Clustering - Data Mining for the Masses - page 99

data set. In that chapter, we discussed filtering out observations as a Data Preparation step, but we
can use the same operator in our Deployment as well. Using the search field in the Operators tab,
locate the Filter Examples operator and connect it to your k-Means Clustering operator, as is
depicted in Figure 6-9. Note that we have not disconnected the clu (cluster) port from the 'res'
(result set) port, but rather, we have connected a second clu port to our exa port on the Filter
Examples operator, and connected the exa port from Filter Examples to its own res port.
Figure 6-9. Filtering our cluster model's output for only observations in cluster 0.
As indicated by the black arrows in Figure 6-9, we are filtering out our observations based on an
attribute filter, using the parameter string cluster=cluster_0. This means that only those
observations in the data set that are classified in the cluster_0 group will be retained. Go ahead
and click the play button to run the model again.
You will see that we have not lost our Cluster Model tab. It is still available to us, but now we
have added an ExampleSet tab, which contains only those 154 observations which fell into cluster
0. As with the result of previous models we've created, we have descriptive statistics for the
various attributes in the data set.