evclust.mecmdd_rwl#
This module contains the main function for Multi-View Evidential C-Medoid clustering (MECMdd) with adaptive weightings with Relevance Weight for each dissimilarity matrix estimated Locally (MECMdd-RWL), i.e for each cluster, using weight Sum (MECMdd-RWL-S) constraint or weight Product (MECMdd-RWL-P) constraint.
Armel Soubeiga, Violaine Antoine and Sylvain Moreno. “Multi-View Relational Evidential C-Medoid Clustering with Adaptive Weighted” 2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)
Module Contents#
- evclust.mecmdd_rwl.mecmdd_rwl(Xlist, c, type='full', alpha=1, beta=1.5, delta=9, epsi=0.0001, disp=True, gamma=0.5, eta=1, weight='sum', s=None)[source]#
Multi-View Evidential C-Medoid clustering (MECMdd) with adaptive weightings with Relevance Weight for each dissimilarity matrix estimated Locally (MECMdd-RWL), i.e for each cluster, using weight Sum (MECMdd-RWL-S) constraint or weight Product (MECMdd-RWL-P) constraint.
Parameters:#
- Xlist (list of np.array):
A list of square symmetric dissimilarity matrices.
- c (int):
Number of clusters to create.
- type (str):
Type of structure (‘full’ by default).
- alpha (float):
Weighting parameter for cardinalities.
- beta (float):
uncertainty exponent for membership degrees.
- delta (float or None):
Precision parameter (used to calculate delta2). If None, distribution of matrix are considere.
- epsi (float):
Convergence threshold.
- disp (bool):
Whether to display the objective function value during iteration.
- gamma (float):
Coefficient to adjust the imprecise contribution.
- eta (float):
Coefficient for outlier identification.
- weight (str):
Type of constraint function using to learn weight (‘sum’ or ‘prod’).
- s (float or None):
Additional weight parameter.
Returns:#
The credal partition (an object of class “credpart”).
Example:#
from evclust.mecmdd_rwl import mecmdd_rwl matrix1 = np.random.rand(5, 5) matrix1 = (matrix1 + matrix1.T) / 2 matrix2 = np.random.rand(5, 5) matrix2 = (matrix2 + matrix2.T) / 2 Xlist = [matrix1, matrix2] clus = mecmdd_rwl(Xlist, c=2, type='simple', alpha=1, beta=1.5, delta=9, epsi=1e-4, disp=True, gamma=0.5, eta=1, weight='prod', s=None) clus['param']['lambda'] # View weight
References:#
Armel Soubeiga, Violaine Antoine and Sylvain Moreno. “Multi-View Relational Evidential C-Medoid Clustering with Adaptive Weighted” 2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA).
See also
extractMass(),makeF(),lambdaInit_local()Note
Keywords : Evidential clustering, credal partition, relational multi-view, belief function MECMdd-RWL is able to address the uncertainty and imprecision of multi-view relational data clustering, and provides a credible partition, which extends fuzzy, possibilistic and rough partitions. It can automatically lern the importance of each views by estimated a weight locally for each cluster in a collaborative learning framework.