# Reliable Agglomerative Clustering

## TL;DR

This paper proposes a new adaptive agglomerative clustering strategy that extracts all reliable linkages at each step, improving flexibility and density consistency, and demonstrates its effectiveness through experiments.

## Contribution

It introduces a novel strategy for agglomerative clustering that considers all reliable linkages, extending standard methods and connecting to minimum spanning tree algorithms.

## Key findings

- The new strategy improves clustering performance on real-world datasets.
- It generalizes standard agglomerative clustering by extracting multiple linkages.
- The approach is applicable with common linkage criteria, including single linkage.

## Abstract

Standard agglomerative clustering suggests establishing a new reliable linkage at every step. However, in order to provide adaptive, density-consistent and flexible solutions, we study extracting all the reliable linkages at each step, instead of the smallest one. Such a strategy can be applied with all common criteria for agglomerative hierarchical clustering. We also study that this strategy with the single linkage criterion yields a minimum spanning tree algorithm. We perform experiments on several real-world datasets to demonstrate the performance of this strategy compared to the standard alternative.