Class represents TTSAS (Two-Threshold Sequential Algorithmic Scheme). More...
Public Member Functions | |
def | __init__ (self, data, threshold1, threshold2, ccore, kwargs) |
Creates TTSAS algorithm. More... | |
def | process (self) |
Performs cluster analysis in line with rules of BSAS algorithm. More... | |
Public Member Functions inherited from pyclustering.cluster.bsas.bsas | |
def | __init__ (self, data, maximum_clusters, threshold, ccore=True, kwargs) |
Creates classical BSAS algorithm. More... | |
def | process (self) |
Performs cluster analysis in line with rules of BSAS algorithm. More... | |
def | get_clusters (self) |
Returns list of allocated clusters, each cluster contains indexes of objects in list of data. More... | |
def | get_representatives (self) |
Returns list of representatives of allocated clusters. More... | |
def | get_cluster_encoding (self) |
Returns clustering result representation type that indicate how clusters are encoded. More... | |
Class represents TTSAS (Two-Threshold Sequential Algorithmic Scheme).
Clustering results of BSAS and MBSAS are strongly dependent on the order in which the points in data. TTSAS helps to overcome this shortcoming by using two threshold parameters. The first - if the distance to the nearest cluster is less than the first threshold then point is assigned to the cluster. The second - if distance to the nearest cluster is greater than the second threshold then new cluster is allocated.
Code example of TTSAS usage:
def pyclustering.cluster.ttsas.ttsas.__init__ | ( | self, | |
data, | |||
threshold1, | |||
threshold2, | |||
ccore, | |||
kwargs | |||
) |
Creates TTSAS algorithm.
[in] | data | (list): Input data that is presented as list of points (objects), each point should be represented by list or tuple. |
[in] | threshold1 | Dissimilarity level (distance) between point and its closest cluster, if the distance is less than 'threshold1' value then point is assigned to the cluster. |
[in] | threshold2 | Dissimilarity level (distance) between point and its closest cluster, if the distance is greater than 'threshold2' value then point is considered as a new cluster. |
[in] | ccore | (bool): If True than DLL CCORE (C++ solution) will be used for solving. |
[in] | **kwargs | Arbitrary keyword arguments (available arguments: 'metric'). |
Keyword Args:
def pyclustering.cluster.ttsas.ttsas.process | ( | self | ) |
Performs cluster analysis in line with rules of BSAS algorithm.