scipy.cluster.vq.kmeans2¶
- scipy.cluster.vq.kmeans2(data, k, iter=10, thresh=1e-05, minit='random', missing='warn')[source]¶
Classify a set of observations into k clusters using the k-means algorithm.
The algorithm attempts to minimize the Euclidian distance between observations and centroids. Several initialization methods are included.
Parameters: data : ndarray
A ‘M’ by ‘N’ array of ‘M’ observations in ‘N’ dimensions or a length ‘M’ array of ‘M’ one-dimensional observations.
k : int or ndarray
The number of clusters to form as well as the number of centroids to generate. If minit initialization string is ‘matrix’, or if a ndarray is given instead, it is interpreted as initial cluster to use instead.
iter : int
Number of iterations of the k-means algrithm to run. Note that this differs in meaning from the iters parameter to the kmeans function.
thresh : float
(not used yet)
minit : string
Method for initialization. Available methods are ‘random’, ‘points’, ‘uniform’, and ‘matrix’:
‘random’: generate k centroids from a Gaussian with mean and variance estimated from the data.
‘points’: choose k observations (rows) at random from data for the initial centroids.
‘uniform’: generate k observations from the data from a uniform distribution defined by the data set (unsupported).
‘matrix’: interpret the k parameter as a k by M (or length k array for one-dimensional data) array of initial centroids.
missing : string
Method to deal with empty clusters. Available methods are ‘warn’ and ‘raise’:
‘warn’: give a warning and continue.
‘raise’: raise an ClusterError and terminate the algorithm.
Returns: centroid : ndarray
A ‘k’ by ‘N’ array of centroids found at the last iteration of k-means.
label : ndarray
label[i] is the code or index of the centroid the i’th observation is closest to.