Question: 1 . The k - Means clustering technique for data analysis is ideal for _ _ _ _ _ _ _ _ _ . machine

1. The k-Means clustering technique for data analysis is ideal for _________.
machine learning
prediction
time series forecasting
segmentation
2. The averages for each attribute in each cluster created by a k-Means model are called _______.
centroids
midpoints
simple means
complex means
3. Thekin k-Means indicates ________.
the coefficient of the dependent variable
the intercept for the model
the coefficient of the independent variable
the number of clusters desired
4. True or false: The cluster numberassigned to each cluster in a k-Mean model indicates the relative importance of each cluster when compared to the others.
5. To view which observations are assigned to each cluster in a k-Means model in RapidMiner, use the ________ feature.
Description
Centroid Table
Graph
Folder View
6. To prevent a k-Means model fora large data set from taking a long time to run, you can adjust the _________ parameter in RapidMiner.
divergence
start values
measure types
max runs
7. To examine all records in one specific cluster in RapidMiner, use a ______________ operator.
Cluster
Select Attributes
Filter Examples
Sample
8. To see the size of each cluster in RapidMiner, click the ______________ icon in Results view.
Graph
Folder View
Description
Centroid Table
9. True or false: There is no need to load a specific library in order to create k-Means models in R.
10. To see which observations fall into each cluster in a k-Means model in R, combine the ________ with the data set in a data frame.
cluster
centers
coefficient
size
11. To view the averages for each attribute in each cluster in a k-Means model in R, put the ________ into a data frame.
centers
cluster
coefficient
size
12. To view how many observations are in each cluster in a k-Means model in R, put the ________ into a data frame.
coefficient
cluster
centers
size

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