evclust.wmvec_fp

This module contains the main function for Adaptive Weighted Multi-View Evidential Clustering With Feature Preference (WMVEC-FP).

Zhe Liu, Haojian Huang, Sukumar Letchmunan, Muhammet Deveci, Adaptive weighted multi-view evidential clustering with feature preference, Knowledge-Based Systems, Volume 294, 2024, 111770, ISSN 0950-7051

Module Contents

evclust.wmvec_fp.wmvec_fp(X, c, alpha=2, delta=5, maxit=20, epsi=0.001, beta=1.1, lmbda=403.4288, gamma=1, type='simple', disp=True)

Adaptive Weighted Multi-View Evidential Clustering With Feature Preference (WMVEC-FP) Algorithm. WMVEC-FP learn the importance of each view and he importance of each feature under different views. WMVEC-FP is based on objets row-data.

Parameters:

X (list of np.ndarray):

List of datasets from different views.

c (int):

Number of clusters.

alpha (float):

Parameter for distance weighting.

beta (float):

Exponent for mass function calculation.

lmbda (float):

Parameter for R update.

delta (list):

List of penalties for empty sets for each view.

epsi (float):

Convergence threshold.

maxit (int):

Maximum number of iterations.

type (str):

Type of focal set matrix to generate (‘simple’, ‘full’, ‘pairs’).

disp (bool):

If True (default), intermediate results are displayed.

Returns:

The credal partition (an object of class “credpart”).

Example:

from evclust.wmvec_fp import wmvec_fp
from evclust.datasets import load_prop

df = load_prop()
clus = wmvec_fp(X=df, c=4, alpha=2, delta=5, maxit=20, epsi=1e-3, beta=1.1, lmbda=403.4288, gamma = 1,
                type="simple", disp=True)

# View weight
clus['param]['R']

# Features relevences
clus['vectorW]['R']

References:

Zhe Liu, Haojian Huang, Sukumar Letchmunan, Muhammet Deveci, Adaptive weighted multi-view evidential clustering with feature preference, Knowledge-Based Systems, Volume 294, 2024, 111770, ISSN 0950-7051

See also

extractMass(), makeF(), update_Aj_fp(), get_distance_fp(), update_M_fp(), update_R_fp(), update_V_fp(), update_W_fp(), update_jaccard_fp(), Centroids_Initialization_fp()

Note

Keywords : Evidential clustering, Multi-view learning, Theory of belief functions, Credal partition WMVEC-FP solve the problems faced by high-dimensional multi-view data clustering. By integrating feature weight learning, view weight learning, and evidential clustering into a unified framework, WMVEC-FP identifies the contributions of different features in each view.

evclust.wmvec_fp.Centroids_Initialization_fp(X, K)

evclust.wmvec_fp.get_distance_fp(mode, view, data, nbFoc, Aj, F_update, alpha, beta, delta, features, *args)

evclust.wmvec_fp.update_Aj_fp(view, cluster, features, Aj, F, center, nbFoc)

evclust.wmvec_fp.update_M_fp(view, data, dis, beta, nbFoc)

evclust.wmvec_fp.update_R_fp(view, dis, lmbda)

evclust.wmvec_fp.update_V_fp(view, cluster, alpha, beta, data, M, R, F_update, features)

evclust.wmvec_fp.update_W_fp(view, dis, features, gamma)

evclust.wmvec_fp.update_jaccard_fp(view, lmbda, gamma, R, W, dis)