Moreau Envelope-Based Clustering for Generalized Multi-Source Weber Problem

TL;DR

This paper introduces a novel clustering algorithm based on the Moreau envelope that automatically determines the number of clusters and efficiently solves a structured optimization problem.

Contribution

It presents a new method leveraging the Moreau envelope for clustering that handles nonconvex, nonsmooth problems and automatically identifies the optimal number of clusters.

Findings

The proposed method is fast and scalable.

It performs competitively in clustering quality.

It is efficient in terms of computational resources.

Abstract

In this paper, we propose an efficient algorithm for data clustering based on the Moreau envelope, which approximates nonsmooth and nonconvex components of the generalized multi-source Weber problem. The number of clusters is not fixed in advance and is determined automatically by progressively removing empty or redundant clusters. The smoothing induced by the Moreau envelope transforms the original problem into a structured optimization task that can be efficiently solved using first-order methods and simple matrix vector operations. Numerical experiments on synthetic and real datasets show that the proposed approach is fast, scalable, and competitive with existing methods in both clustering quality and computational efficiency.