> The Spider Objects

>       KMEANS k-means clustering object

>        A=KMEANS(H) returns a k-means clustering object
>        initialized with hyperparameters H.

>        Hyperparameters and their defaults:
>          k=2              -- the number of clusters
>          a.child=distance -- distance measure to use (a distance object)
>          max_loops=1000   -- maximum number of iterations of training

>        Model:
>          mu               -- cluster centers
>          y                -- cluster assignment of each training example

>        Methods:
>          training: cluster a dataset
>          testing:  assign points to clusters according to the nearest
>                    cluster center
>          distortion: get value of distortion

>        Example:
>         d=gen(spiral({'m=200','n=0.5','noise=0.35'}));
>         [r,a] =train(kmeans,d)
>         I=find(r.X==1);clf;hold on;
>         plot(d.X(I,1),d.X(I,2),'r.');
>         I=find(r.X==2);
>         plot(d.X(I,1),d.X(I,2),'b.');
>        ['compare with spectral clustering ']

> | Reference : chapter 10 (Richard O. Duda and Peter E. Hart) Unsupervised learning and clustering

> | Author : Richard O. Duda , Peter E. Hart

> | Link : http://www.amazon.com/exec/obidos/tg/detail/-/0471056693/002-6279399-2828812?v=glance