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

Class represents clustering algorithm KMedians. More...
Public Member Functions  
def  __init__ (self, data, initial_medians, tolerance=0.001, ccore=True, **kwargs) 
Constructor of clustering algorithm KMedians. More...  
def  process (self) 
Performs cluster analysis in line with rules of KMedians algorithm. More...  
def  predict (self, points) 
Calculates the closest cluster to each point. More...  
def  get_clusters (self) 
Returns list of allocated clusters, each cluster contains indexes of objects in list of data. More...  
def  get_medians (self) 
Returns list of centers of allocated clusters. More...  
def  get_total_wce (self) 
Returns sum of metric errors that depends on metric that was used for clustering (by default SSE  Sum of Squared Errors). More...  
def  get_cluster_encoding (self) 
Returns clustering result representation type that indicate how clusters are encoded. More...  
Class represents clustering algorithm KMedians.
The algorithm is less sensitive to outliers than KMeans. Medians are calculated instead of centroids.
Example:
Definition at line 26 of file kmedians.py.
def pyclustering.cluster.kmedians.kmedians.__init__  (  self,  
data,  
initial_medians,  
tolerance = 0.001 , 

ccore = True , 

**  kwargs  
) 
Constructor of clustering algorithm KMedians.
[in]  data  (list): Input data that is presented as list of points (objects), each point should be represented by list or tuple. 
[in]  initial_medians  (list): Initial coordinates of medians of clusters that are represented by list: [center1, center2, ...]. 
[in]  tolerance  (double): Stop condition: if maximum value of change of centers of clusters is less than tolerance than algorithm will stop processing 
[in]  ccore  (bool): Defines should be CCORE library (C++ pyclustering library) used instead of Python code or not. 
[in]  **kwargs  Arbitrary keyword arguments (available arguments: 'metric', 'itermax'). 
Keyword Args:
Definition at line 60 of file kmedians.py.
def pyclustering.cluster.kmedians.kmedians.get_cluster_encoding  (  self  ) 
Returns clustering result representation type that indicate how clusters are encoded.
Definition at line 219 of file kmedians.py.
def pyclustering.cluster.kmedians.kmedians.get_clusters  (  self  ) 
Returns list of allocated clusters, each cluster contains indexes of objects in list of data.
Definition at line 178 of file kmedians.py.
Referenced by pyclustering.samples.answer_reader.get_cluster_lengths(), and pyclustering.cluster.optics.optics.process().
def pyclustering.cluster.kmedians.kmedians.get_medians  (  self  ) 
Returns list of centers of allocated clusters.
Definition at line 190 of file kmedians.py.
def pyclustering.cluster.kmedians.kmedians.get_total_wce  (  self  ) 
Returns sum of metric errors that depends on metric that was used for clustering (by default SSE  Sum of Squared Errors).
Sum of metric errors is calculated using distance between point and its center:
\[error=\sum_{i=0}^{N}distance(x_{i}center(x_{i}))\]
Definition at line 205 of file kmedians.py.
def pyclustering.cluster.kmedians.kmedians.predict  (  self,  
points  
) 
Calculates the closest cluster to each point.
[in]  points  (array_like): Points for which closest clusters are calculated. 
An example how to calculate (or predict) the closest cluster to specified points.
Definition at line 133 of file kmedians.py.
def pyclustering.cluster.kmedians.kmedians.process  (  self  ) 
Performs cluster analysis in line with rules of KMedians algorithm.
Definition at line 93 of file kmedians.py.