pyclustering
0.10.1
pyclustring is a Python, C++ data mining library.

ExpectationMaximization clustering algorithm for Gaussian Mixture Model (GMM). More...
Public Member Functions  
def  __init__ (self, data, amount_clusters, means=None, variances=None, observer=None, tolerance=0.00001, iterations=100) 
Initializes ExpectationMaximization algorithm for cluster analysis. More...  
def  process (self) 
Run clustering process of the algorithm. More...  
def  get_clusters (self) 
def  get_centers (self) 
def  get_covariances (self) 
def  get_probabilities (self) 
Returns 2dimensional list with belong probability of each object from data to cluster correspondingly, where that first index is for cluster and the second is for point. More...  
ExpectationMaximization clustering algorithm for Gaussian Mixture Model (GMM).
The algorithm provides only clustering services (unsupervised learning). Here an example of data clustering process:
Here is clustering results of the ExpectationMaximization clustering algorithm where popular sample 'OldFaithful' was used. Initial random means and covariances were used in the example. The first step is presented on the left side of the figure and final result (the last step) is on the right side:
def pyclustering.cluster.ema.ema.__init__  (  self,  
data,  
amount_clusters,  
means = None , 

variances = None , 

observer = None , 

tolerance = 0.00001 , 

iterations = 100 

) 
Initializes ExpectationMaximization algorithm for cluster analysis.
[in]  data  (list): Dataset that should be analysed and where each point (object) is represented by the list of coordinates. 
[in]  amount_clusters  (uint): Amount of clusters that should be allocated. 
[in]  means  (list): Initial means of clusters (amount of means should be equal to amount of clusters for allocation). If this parameter is 'None' then KMeans algorithm with KMeans++ method will be used for initialization by default. 
[in]  variances  (list): Initial cluster variances (or covariances in case of multidimensional data). Amount of covariances should be equal to amount of clusters that should be allocated. If this parameter is 'None' then KMeans algorithm with KMeans++ method will be used for initialization by default. 
[in]  observer  (ema_observer): Observer for gathering information about clustering process. 
[in]  tolerance  (float): Defines stop condition of the algorithm (when difference between current and previous loglikelihood estimation is less then 'tolerance' then clustering is over). 
[in]  iterations  (uint): Additional stop condition parameter that defines maximum number of steps that can be performed by the algorithm during clustering process. 
def pyclustering.cluster.ema.ema.get_centers  (  self  ) 
def pyclustering.cluster.ema.ema.get_clusters  (  self  ) 
Definition at line 533 of file ema.py.
Referenced by pyclustering.samples.answer_reader.get_cluster_lengths(), and pyclustering.cluster.optics.optics.process().
def pyclustering.cluster.ema.ema.get_covariances  (  self  ) 
def pyclustering.cluster.ema.ema.get_probabilities  (  self  ) 
Returns 2dimensional list with belong probability of each object from data to cluster correspondingly, where that first index is for cluster and the second is for point.
def pyclustering.cluster.ema.ema.process  (  self  ) 