Fast Dempster-Shafer clustering using a neural network structure

TL;DR

This paper introduces a neural network-based approach for fast clustering in Dempster-Shafer theory, significantly reducing computational complexity for large-scale problems while maintaining effective clustering quality.

Contribution

The paper presents a novel neural network structure for Dempster-Shafer clustering that outperforms previous iterative methods in speed, especially for large problems.

Findings

Neural network approach is faster than iterative optimization for large problems.

The method finds a global minimum in multiple runs for up to six clusters.

Metaconflict growth is moderate despite faster increase in neural network method.

Abstract

In this paper we study a problem within Dempster-Shafer theory where 2**n - 1 pieces of evidence are clustered by a neural structure into n clusters. The clustering is done by minimizing a metaconflict function. Previously we developed a method based on iterative optimization. However, for large scale problems we need a method with lower computational complexity. The neural structure was found to be effective and much faster than iterative optimization for larger problems. While the growth in metaconflict was faster for the neural structure compared with iterative optimization in medium sized problems, the metaconflict per cluster and evidence was moderate. The neural structure was able to find a global minimum over ten runs for problem sizes up to six clusters.