Here’s your chance to prove what you learned
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Question 1 of 4
The clustering has failed to separate the data properly
The data points are likely assigned to the wrong clusters
To group similar data points into clusters without predefined labels
To reduce the dimensionality of high-dimensional data
Question 2 of 4
Data points are assigned random labels that never change
Centroids are fixed while data points move between clusters
Data points are assigned to the nearest centroid, then centroids are recalculated based on new assignments
The algorithm removes outliers until only the most common data points remain
Question 3 of 4
A technique to speed up the clustering algorithm
A way to identify and remove outliers before clustering
A method to determine the optimal number of clusters (K)
An approach to visualize high-dimensional data
Question 4 of 4
What does a Silhouette Score close to 1 indicate in K-means clustering?
The data points are well-clustered with clear separation between clusters
There is too much overlap between different clusters