M-File Help: kmeans View code for kmeans

kmeans

K-means clustering

[L,C] = kmeans(x, k, options) is a k-means clustering of multi-dimensional data points x (DxN) where N is the number of points, and D is the dimension. The data is organized into k clusters based on Euclidean distance from cluster centres C (DxK). L is a vector (Nx1) whose elements indicates which cluster the corresponding element of x belongs to.

[L,C] = kmeans(x, k, c0) as above but the initial clusters c0 (DxK) is given and column I is the initial estimate of the centre of cluster I.

L = kmeans(x, C) is similar to above but the clustering step is not performed, it is assumed to have been completed previously. C (DxK) contains the cluster centroids and L (Nx1) indicates which cluster the corresponding element of x is closest to.

Options

'random' initial cluster centres are chosen randomly from the set of data points X
'spread' initial cluster centres are chosen randomly from within the hypercube spanned by X.

Reference

"Pattern Recognition Principles", Tou and Gonzalez, Addison-Wesley 1977, pp 94


 

© 1990-2012 Peter Corke.