function [L,C] = kmeans(X,k)

KMEANS Cluster multivariate data using the k-means++ algorithm.

[L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class

label per column in X and a size(X,1)-by-k matrix C containing the

centers corresponding to each class.



Version: 07/08/11

Authors: Laurent Sorber (Laurent.Sorber@cs.kuleuven.be)



References:

[1] J. B. MacQueen, "Some Methods for Classification and Analysis of

MultiVariate Observations", in Proc. of the fifth Berkeley

Symposium on Mathematical Statistics and Probability, L. M. L. Cam

and J. Neyman, eds., vol. 1, UC Press, 1967, pp. 281-297.

[2] D. Arthur and S. Vassilvitskii, "k-means++: The Advantages of

Careful Seeding", Technical Report 2006-13, Stanford InfoLab, 2006.

-function [L,C] = kmeans(X,k)

KMEANS Cluster multivariate data using the k-means++ algorithm.

　　[L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class

　　label per column in X and a size(X,1)-by-k matrix C containing the

　　centers corresponding to each class.



　　Version: 07/08/11

　　Authors: Laurent Sorber (Laurent.Sorber@cs.kuleuven.be)



　　References:

　　[1] J. B. MacQueen, "Some Methods for Classification and Analysis of

　　　　MultiVariate Observations", in Proc. of the fifth Berkeley

　　　　Symposium on Mathematical Statistics and Probability, L. M. L. Cam

　　　　and J. Neyman, eds., vol. 1, UC Press, 1967, pp. 281-297.

　　[2] D. Arthur and S. Vassilvitskii, "k-means++: The Advantages of

　　　　Careful Seeding", Technical Report 2006-13, Stanford InfoLab, 2006.