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Sagot :
The stated statement, "K-means clustering method surpasses hierarchical clustering techniques in terms of processing efficiency when working with huge data sets," is TRUE.
What is the k-means clustering method?
The goal of k-means clustering, a vector quantization technique that originated in signal processing, is to divide n observations into k clusters, where each observation belongs to the cluster that has the closest mean (also known as the cluster centroid or cluster center), which serves as a prototype for the cluster.
The result is the division of the data space into Voronoi cells.
Euclidean distances can only be minimized via the geometric median; the more difficult Weber problem can only be solved using normal Euclidean distances. K-means clustering minimizes within-cluster variances (squared Euclidean distances).
For instance, it is possible to obtain better Euclidean solutions using k-medians and k-medoids.
When dealing with large data sets, the k-means clustering method outperforms the hierarchical clustering techniques in terms of processing efficiency.
Therefore, the stated statement, "K-means clustering algorithm surpasses hierarchical clustering techniques in terms of processing efficiency when working with huge data sets," is TRUE.
Know more about the k-means clustering method here:
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