Data Mining by Mehmed Kantardzic

Author:Mehmed Kantardzic
Language: eng
Format: mobi, epub
ISBN: 9780470890455
Publisher: Wiley-IEEE Press
Published: 2011-06-28T10:00:00+00:00

Therefore, x2 belongs to the cluster C1. The new centroid will be

(b) The third sample x3 is compared with the centroid M1 (still the only centroid):

(c) The fourth sample x4 is compared with the centroid M1:

Because the distance of the sample from the given centroid M1 is larger than the threshold value δ, this sample will create its own cluster C2 = {x4} with the corresponding centroid M2 = {5, 0}.

(d) The fifth sample x5 is compared with both cluster centroids:

The sample is closer to the centroid M2, and its distance is less than the threshold value δ. Therefore, sample x5 is added to the second cluster C2:

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