Hard Clustering

Hard clustering aims to assign data points to distinct, non-overlapping groups, optimizing for criteria like minimizing within-cluster distances or maximizing inter-cluster separation. Recent research emphasizes developing robust algorithms, such as variations of k-means and Bregman clustering, that address challenges like imbalanced data, high dimensionality, and the selection of the optimal number of clusters, often incorporating techniques from information theory and optimal transport. These advancements improve clustering accuracy and efficiency across diverse applications, including federated learning, network analysis, and large language model compression, by enabling more effective data summarization and representation learning. Furthermore, research is focused on developing better evaluation metrics for comparing different clustering results, particularly in the context of fuzzy clustering.

Papers

September 11, 2024

k-MLE, k-Bregman, k-VARs: Theory, Convergence, Computation
Zuogong Yue, Victor Solo
Early Stage Convergence Computation Method Distance Matter Real World Datasets Bregman Information Hard Clustering Vector Autoregressive

June 13, 2024

EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters
Vasileios Tsouvalas, Samaneh Mohammadi, Ali Balador, Tanir Ozcelebi, Francesco Flammini, Nirvana Meratnia
Differential Privacy Secure Aggregation Bayesian Filtering Hard Clustering Functional Encryption

February 23, 2024

Universal Lower Bounds and Optimal Rates: Achieving Minimax Clustering Error in Sub-Exponential Mixture Models
Maximilien Dreveton, Alperen Gözeten, Matthias Grossglauser, Patrick Thiran
Lower Bound Mixture Model Gaussian Mixture Optimal Rate Hard Clustering Multinomial Mixture

February 22, 2024

Imbalanced Data Clustering using Equilibrium K-Means
Yudong He
K Mean Hard Clustering Fuzzy C Mean Imbalanced Clustering

February 3, 2024

A Bayesian cluster validity index
Nathakhun Wiroonsri, Onthada Preedasawakul
Hard Clustering Clustering Validation Dirichlet Prior

December 16, 2023

Random Models for Fuzzy Clustering Similarity Measures
Ryan DeWolfe, Jeffery L. Andrews
Hard Clustering Fuzzy Clustering Market Index Random Model

November 24, 2023

Stable Cluster Discrimination for Deep Clustering
Qi Qian
Deep Clustering Hard Clustering

October 30, 2023

Exact Recovery and Bregman Hard Clustering of Node-Attributed Stochastic Block Model
Maximilien Dreveton, Felipe S. Fernandes, Daniel R. Figueiredo
Sparse Network Stochastic Block Model Hard Clustering Interaction Network Attributed Network Exact Community Recovery Iterative Clustering

October 19, 2023

DCSI -- An improved measure of cluster separability based on separation and connectedness
Jana Gauss, Fabian Scheipl, Moritz Herrmann
Separation Performance Connected Behavior Density Based Clustering Hard Clustering Clustering Validation

October 5, 2023

Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein
Hugues Van Assel, Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Nicolas Courty
Dimensionality Reduction Data Dimensionality Constructive Reduction Hard Clustering Gromov Wasserstein

September 2, 2023

eDKM: An Efficient and Accurate Train-time Weight Clustering for Large Language Models
Minsik Cho, Keivan A. Vahid, Qichen Fu, Saurabh Adya, Carlo C Del Mundo, Mohammad Rastegari, Devang Naik, Peter Zatloukal
LLM Fine Tuning LLM Compression Hard Clustering

August 12, 2023

Latent Random Steps as Relaxations of Max-Cut, Min-Cut, and More
Sudhanshu Chanpuriya, Cameron Musco
Non Negative Matrix Factorization Hard Clustering Node Clustering Confidence Relaxation Soft Clustering

July 25, 2023

DBGSA: A Novel Data Adaptive Bregman Clustering Algorithm
Ying Xiao, Hou-biao Li, Yu-pu Zhang
Clustering Algorithm Bregman Divergence State of the Art Clustering Hard Clustering Gravitational Search Algorithm

August 27, 2022

Geometrical Homogeneous Clustering for Image Data Reduction
Shril Mody, Janvi Thakkar, Devvrat Joshi, Siddharth Soni, Rohan Patil, Nipun Batra
MNIST Dataset Data Reduction Hard Clustering Projective Clustering

August 2, 2022

Cluster Weighted Model Based on TSNE algorithm for High-Dimensional Data
Kehinde Olobatuyi
Dimensionality Reduction High Dimensional Data Mixture Model Hard Clustering Stochastic Neighbor Embedding

June 22, 2022

Bregman Power k-Means for Clustering Exponential Family Data
Adithya Vellal, Saptarshi Chakraborty, Jason Xu
Bregman Divergence Exponential Family Hard Clustering Cluster Shape

June 20, 2022

A Distributional Approach for Soft Clustering Comparison and Evaluation
Andrea Campagner, Davide Ciucci, Thierry Denœux
Global Evaluation Metric Space Hard Clustering Soft Clustering Distributional Approach

April 23, 2022

Selective clustering ensemble based on kappa and F-score
Jie Yan, Xin Liu, Ji Qi, Tao You, Zhong-Yuan Zhang
Different Cluster K Space Selective Classification Hard Clustering Ensemble Clustering C Score Selective Ensemble

April 5, 2022

Pareto-optimal clustering with the primal deterministic information bottleneck
Andrew K. Tan, Max Tegmark, Isaac L. Chuang
Sparsity Increase Information Bottleneck Lossy Compression Pareto Frontier Hard Clustering

February 1, 2022

Gradient Based Clustering
Aleksandar Armacki, Dragana Bajovic, Dusan Jakovetic, Soummya Kar
Natural Gradient Data Clustering Hard Clustering Cluster Assignment Clustering Problem Voronoi Partition