Kmeans Clustering¶
Clusters data by trying to separate samples in n groups of equal variance
Configuration: 


Attributes: 

Inputs:  
Outputs: 

Ports:
Outputs:
model: model
Model
Configuration:
 n_clusters
 The number of clusters to form as well as the number of centroids to generate.
 max_iter
 Maximum number of iterations of the kmeans algorithm for a single run.
 n_init
 Number of time the kmeans algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.
 init
Method for initialization, defaults to ‘kmeans++’:
‘kmeans++’ : selects initial cluster centers for kmean clustering in a smart way to speed up convergence. See section Notes in k_init for more details.
‘random’: choose k observations (rows) at random from data for the initial centroids.
If an ndarray is passed, it should be of shape (n_clusters, n_features) and gives the initial centers.
 algorithm
 Kmeans algorithm to use. The classical EMstyle algorithm is “full”. The “elkan” variation is more efficient by using the triangle inequality, but currently doesn’t support sparse data. “auto” chooses “elkan” for dense data and “full” for sparse data.
 precompute_distances
Precompute distances (faster but takes more memory).
‘auto’ : do not precompute distances if n_samples * n_clusters > 12 million. This corresponds to about 100MB overhead per job using double precision.
True : always precompute distances
False : never precompute distances
 tol
 Relative tolerance with regards to inertia to declare convergence
 n_jobs
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 are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below 1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = 2, all CPUs but one are used.
 random_state
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
Some of the docstrings for this module have been automatically extracted from the scikitlearn library and are covered by their respective licenses.