K-means clustering algorithm

K-means clustering algorithm - ISCAS 2007

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Discussion

• We note that the results are subject to sampling errors (also

see the “bonus” 4-group 2d example in the Lecture 20 notes)

• The jump value of the transformed distortion does get us to

the neighborhood of correct K (the high values are at K=17,

20, 22, 25)

• Because the peak occurs at K=25, we really should have ran a

few more runs at larger values of K

• By comparison, the jump value of the inverted distortion

selected a one-cluster result as best description

Results • I expect to see the jump value of the transformed distortion to peak at K=21 • I did not get what I expected to see • Although not shown here, two other examples show similar results

Discussion • We note that the results are subject to sampling errors (also see the “bonus” 4-group 2d example in the Lecture 20 notes) • The jump value of the transformed distortion does get us to the neighborhood of correct K (the high values are at K=17, 20, 22, 25) • Because the peak occurs at K=25, we really should have ran a few more runs at larger values of K • By comparison, the jump value of the inverted distortion selected a one-cluster result as best description

Page 1 and 2: K-means clustering algorithm Once t
Page 3 and 4: How to set k in k-means clustering
Page 5 and 6: Results • Run k-means for K=1, 2,
Page 7 and 8: Transformed Distortion 4.5E-11 4E-1
Page 9: 4.5E-11 4E-11 3.5E-11 Transformed D

K-means clustering algorithm

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