NN-EVCLUS: Neural Network-based Evidential Clustering

TL;DR

NN-EVCLUS introduces a neural network-based evidential clustering method that effectively maps attribute vectors to mass functions, improving clustering accuracy and robustness, especially with outliers and constraints.

Contribution

The paper presents a novel neural network approach for evidential clustering that incorporates conflict minimization and can handle outliers and constraints.

Findings

Outperforms state-of-the-art evidential clustering algorithms.

Effectively handles outliers and novelty detection.

Improves clustering accuracy on various datasets.

Abstract

Evidential clustering is an approach to clustering based on the use of Dempster-Shafer mass functions to represent cluster-membership uncertainty. In this paper, we introduce a neural-network based evidential clustering algorithm, called NN-EVCLUS, which learns a mapping from attribute vectors to mass functions, in such a way that more similar inputs are mapped to output mass functions with a lower degree of conflict. The neural network can be paired with a one-class support vector machine to make it robust to outliers and allow for novelty detection. The network is trained to minimize the discrepancy between dissimilarities and degrees of conflict for all or some object pairs. Additional terms can be added to the loss function to account for pairwise constraints or labeled data, which can also be used to adapt the metric. Comparative experiments show the superiority of N-EVCLUS over state-of-the-art evidential clustering algorithms for a range of unsupervised and constrained clustering tasks involving both attribute and dissimilarity data.