KMEANSK Performs K-means clustering given a list of feature vectors and k

The argument k indicates the number of clusters you want the data to be

divided into. data_vecs (N*R) is the set of R dimensional feature

vectors for N data points. Each row in data_vecs gives the R

dimensional vector for a single data point. Each column in data_vecs

refers to values for a particular feature vector for all the N data

points. The output data_idxs is a N*1 vector of integers telling which

cluster number a particular data point belongs to. It also outputs

centroids which is a k*R matrix, where each rows gives the vector for

the cluster center. If we want to segment a color image i into 5

clusters using spacial and color information, we can use this function

as follows:

-KMEANSK Performs K-means clustering given a list of feature vectors and k

　The argument k indicates the number of clusters you want the data to be

　divided into. data_vecs (N*R) is the set of R dimensional feature

　vectors for N data points. Each row in data_vecs gives the R

　dimensional vector for a single data point. Each column in data_vecs

　refers to values for a particular feature vector for all the N data

　points. The output data_idxs is a N*1 vector of integers telling which

　cluster number a particular data point belongs to. It also outputs

　centroids which is a k*R matrix, where each rows gives the vector for

　the cluster center. If we want to segment a color image i into 5

　clusters using spacial and color information, we can use this function

　as follows: