SciPy K-Means

SciPy K-Means : Package scipy.cluster.vp provides kmeans() function to perform k-means on a set of observation vectors forming k clusters. In this tutorial, we shall learn the syntax and the usage of kmeans() function with SciPy K-Means Examples.


centroids,distortion = scipy.cluster.vq.kmeans(obs, k_or_guess, iter=20, thresh=1e-05, check_finite=True)
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ParameterOptional/ Required[datatype] Description
obsRequired[ndarray] Each row of the M by N array is an observation vector. The columns are the features seen during each observation. The features must be whitened first with the whiten function.
k_or_guessRequired[int or ndarray] The number of centroids to generate. A code is assigned to each centroid, which is also the row index of the centroid in the code_book matrix generated.
iterOptional[int] The number of times to run k-means, returning the codebook with the lowest distortion. This argument is ignored if initial centroids are specified with an array for the k_or_guess parameter. This parameter does not represent the number of iterations of the k-means algorithm.
threshOptional[float] Terminates the k-means algorithm if the change in distortion since the last k-means iteration is less than or equal to thresh.
check_finiteOptional[boolean] Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. Default: True
centroids[Returned value][ndarray] A k by N array of k centroids.
distortion [Returned Value][float] The distortion between the observations passed and the centroids generated.

Values provided for the optional arguments are default values.

SciPy K-Means Example

In this example, we shall generate a set of random 2-D points, centered around 3 centroids.

# import numpy
from numpy import vstack,array
from numpy.random import rand

# matplotlib 
import matplotlib.pyplot as plt

# scipy
from scipy.cluster.vq import kmeans,vq,whiten

data = vstack(((rand(20,2)+1),(rand(20,2)+3),(rand(20,2)+4.5)))

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SciPy K-Means Example
# whiten the features
data = whiten(data)

# find 3 clusters in the data
centroids,distortion = kmeans(data,3)

print('centroids  : ',centroids)
print('distortion :',distortion)
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centroids  :  [[ 1.42125469  1.58213817]
 [ 3.55399219  3.53655637]
 [ 4.91171555  5.02202473]]
distortion : 0.35623898893
SciPy K-Means Example