Learning materials for Scikit-learn bags

Http://scikit-learn.org/stable/modules/clustering.html#k-means

http://my.oschina.net/u/175377/blog/84420

K-means Clustering Parameter Description:

Http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans

class Sklearn.cluster. Kmeans (n_clusters=8, init= ' k-means++ ', n_init=10, max_iter=300, tol= 0.0001,precompute_distances= ' auto ', verbose=0, random_state=none, copy _x=true, n_jobs=1)

n_clusters : int, optional, default:8

The number of clusters to form as well as the number of centroids to generate.

max_iter : int, default:300

Maximum number of iterations of the K-means algorithm for a single run.

n_init : int, default:10

Number of time the K-means algorithm would be run with different centroid seeds. The final results be is the best output of n_init consecutive runs in terms of inertia.

init : {' k-means++ ', ' random ' or an ndarray}

Method for initialization, defaults to ' k-means++ ':

' k-means++ ': Selects initial cluster Centers for K-mean clustering in a smart-to-speed-up convergence. See sections Notes in K_init for more details.

' Random ': Choose K Observations (rows) at random from data for the initial centroids.

If an Ndarray are passed, it should be of shape (n_clusters, n_features) and gives the initial centers.

precompute_distances : {' auto ', True, False}

Precompute distances (faster but takes more memory).

' Auto ': Do not precompute distances if n_samples * n_clusters > million. This corresponds to about 100MB overhead per job using a double precision.

True:always Precompute Distances

False:never Precompute Distances

tol : float, default:1e-4

Relative tolerance with regards to inertia to declare convergence

n_jobs : int

The number of jobs to use for the computation. This works by computing each of the n_init runs in parallel.

If-1 all CPUs is used. If 1 is given, no parallel computing code was used at all, and which is useful for debugging. For N_jobs Below-1, (N_cpus + 1 + n_jobs) is used. Thus for n_jobs =-2, all CPUs but one is used.

random_state : Integer or NumPy. Randomstate, optional

The generator used to initialize the centers. If An integer is given, it fixes the seed. Defaults to the global numpy random number generator.

verbose : int, default 0

verbosity mode.

copy_x  : boolean, default True blockquote> When pre-computing distances it was more numerically accurate to center the data first. If copy_x is True and then the original data was not modified. If False, the original data is modified, and put back before the function returns, but small numerical differences could be Introduced by subtracting and then adding the data mean.
Cluster_centers_: Array, [N_clusters, N_features] Coordinates of cluster centers Labels_:: Labels of each point Inertia_: Float Sum of distances of samples to their closest cluster center.

Learning materials for Scikit-learn bags