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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.

Sagot :

GinnyAnsweridn

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.


Learn more about k-means clustering here:

https://brainly.com/question/37817178


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