Which is better grouping in terms of within-cluster variance

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Reference no: EM133185466

Quesiton: We have a dataset with 8 two-dimensional points: A = (3,1), B = (5,1), C = (4,2), D = (5,2), E = (2,5), F = (7,4), G = (1,0), H = (8,0). Use K-means and the Euclidean distance to cluster this dataset into 2 clusters.

(a) If the initial cluster centroids are at (1,2) and (1,4), what are the final clusters?
Show the step-by-step calculations.

(b) If the initial cluster centroids are at (3,4) and (6,4), what are the final clusters?
Show the step-by-step calculations.

(c) Between the two results from a) and b), which is a better grouping in terms of within-cluster variance? Justify your answer.

Reference no: EM133185466

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