K-means clustering algorithm

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How to set k in k-means clustering • 17-dimensional data set; i.e., p=17 • 12,000 vectors • The data set has 21 groups – Group 0 has prior probability 0.20 – The remaining 20 groups have equal probability (0.04) • Each group has the same Gaussian density that differs only in the group mean • The group covariance matrix is the identity matrix; i.e., features are pairwise uncorrelated
Results • Run k-means for K=1, 2, …, 25 • After each run, – Compute the mean squared distance to the corresponding cluster center as the total distortion – Compute transformed distortion – Compute the jump of the transformed distortion – Compute the inverse of the distortion (for comparison) – Compute the jump of the inverted distortion
Page 1 and 2: K-means clustering algorithm Once t
Page 3: How to set k in k-means clustering
Page 7 and 8: Transformed Distortion 4.5E-11 4E-1
Page 9 and 10: 4.5E-11 4E-11 3.5E-11 Transformed D
Page 11: Discussion • We note that the res

K-means clustering algorithm

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