Answered

Which one is NOT TRUE about k-means clustering?

A. K-means divides the data into non-overlapping clusters without any cluster internal structure.
B. The objective of k-means is to form clusters in such a way that similar samples go into a cluster and dissimilar samples fall into different clusters.
C. As k-means is an iterative algorithm, it guarantees that it will always converge to the global optimum.



Answer :

GinnyAnswer

Final answer:

K-means clustering minimizes 'within group sum of squares' to group similar samples, but it does not always converge to the global optimum.


Explanation:

K-means clustering is capable of dividing data into non-overlapping clusters to minimize the 'within group sum of squares.' The objective of k-means is to group similar samples together by minimizing the distance to cluster centers.

One statement that is NOT TRUE about k-means clustering is that it guarantees convergence to the global optimum each time due to the nature of local optima, which can lead to slightly different results upon initialization.


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