8 citations · 17 across the 12 of their papers we have counts for
5 papers · 1 filter
Hierarchical Clustering Can Jointly Satisfy Richness, Consistency, and Scale Invariance
Daichi Kuroda, Maximilien Dreveton, Matthias Grossglauser +1
Despite its ubiquity, clustering lacks a universally accepted definition of what is a cluster. Kleinberg's Impossibility Theorem formalizes this difficulty by showing that no flat…
Hierarchical Linkage Clustering Beyond Binary Trees and Ultrametrics
Maximilien Dreveton, Matthias Grossglauser, Daichi Kuroda +1
Hierarchical clustering seeks to uncover nested structures in data by constructing a tree of clusters, where deeper levels reveal finer-grained relationships. Traditional methods,…
Optimal Graph Clustering without Edge Density Signals
Maximilien Dreveton, Elaine Siyu Liu, Matthias Grossglauser +1
This paper establishes the theoretical limits of graph clustering under the Popularity-Adjusted Block Model (PABM), addressing limitations of existing models. In contrast to the St…
Higher-Order Spectral Clustering for Geometric Graphs
Konstantin Avrachenkov, Andrei Bobu, Maximilien Dreveton
The present paper is devoted to clustering geometric graphs. While the standard spectral clustering is often not effective for geometric graphs, we present an effective generalizat…
Almost exact recovery in noisy semi-supervised learning
Konstantin Avrachenkov, Maximilien Dreveton
Graph-based semi-supervised learning methods combine the graph structure and labeled data to classify unlabeled data. In this work, we study the effect of a noisy oracle on classif…