Robust Clustering

Robust clustering aims to develop algorithms that accurately group data points into clusters even in the presence of noise, outliers, or high dimensionality, overcoming limitations of traditional methods like K-means. Current research focuses on developing self-supervised and deep learning approaches, incorporating techniques like graph-based methods, contrastive learning, and robust regularization to improve clustering accuracy and efficiency, particularly for unstructured and high-dimensional data. These advancements are crucial for various applications, including federated learning, where robust clustering helps mitigate privacy concerns and improve model performance across heterogeneous data distributions, and for improving the robustness of multi-task learning. The development of robust clustering methods is significantly impacting data analysis and machine learning by enabling more reliable and accurate insights from complex datasets.

Papers

October 29, 2024

A Dual Adaptive Assignment Approach for Robust Graph-Based Clustering
Yang Xiang, Li Fan, Tulika Saha, Xiaoying Pang, Yushan Pan, Haiyang Zhang, Chengtao Ji
Robust Version Graph Clustering Network Analysis Deep Graph Robust Clustering Graph Based Clustering

October 9, 2024

Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare
Xenia Konti, Hans Riess, Manos Giannopoulos, Yi Shen, Michael J. Pencina, Nicoleta J. Economou-Zavlanos, Michael M. Zavlanos
Case Study Federated Learning Healthcare System Heterogeneous Data Robust Clustering Clustered Federated Learning Client Data Heterogeneity

September 3, 2024

Robust Clustering on High-Dimensional Data with Stochastic Quantization
Anton Kozyriev, Vladimir Norkin
High Dimensional Data Quantization Technique Mini Batch Gradient Descent Robust Clustering Stochastic Quantization

July 29, 2024

Adaptive Self-supervised Robust Clustering for Unstructured Data with Unknown Cluster Number
Chen-Lu Ding, Jiancan Wu, Wei Lin, Shiyang Shen, Xiang Wang, Yancheng Yuan
Unstructured Data Deep Clustering Clustering Result State of the Art Clustering Robust Clustering Cluster Number

May 29, 2024

Mitigating Disparate Impact of Differential Privacy in Federated Learning through Robust Clustering
Saber Malekmohammadi, Afaf Taik, Golnoosh Farnadi
Differential Privacy Decentralized Machine Learning Privacy Sensitive Robust Clustering Disparate Impact Differentially Private Federated Learning

April 4, 2024

Multi-task learning via robust regularized clustering with non-convex group penalties
Akira Okazaki, Shuichi Kawano
Multi Task Learning Multi Task Data Clustering Robust Clustering Outlier Detection Task Non Convex Penalty Robust Task

February 24, 2024

Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation With Heterogeneous Changes
Danial Yazdani, Juergen Branke, Mohammad Sadegh Khorshidi, Mohammad Nabi Omidvar, Xiaodong Li, Amir H. Gandomi, Xin Yao
New Framework Dynamic Environment Benchmark Dataset Dataset Generation Optimal Clustering Robust Clustering Heterogeneous Effect

December 5, 2023

Robust Clustering using Hyperdimensional Computing
Lulu Ge, Keshab K. Parhi
Hyperdimensional Computing Robust Clustering Cluster Hypervectors

November 1, 2023

Enhancing Clustering Representations with Positive Proximity and Cluster Dispersion Learning
Abhishek Kumar, Dong-Gyu Lee
Deep Clustering Clustering Friendly Representation Robust Clustering Close Proximity Learning Based Point Instance Level Loss Clustering Benchmark

August 28, 2023

Some issues in robust clustering
Christian Hennig
Data Clustering Gaussian Mixture Model Proper Issue Labeling Global Outlier Robust Clustering

July 29, 2023

Efficient Semi-Supervised Federated Learning for Heterogeneous Participants
Zhipeng Sun, Yang Xu, Hongli Xu, Zhiyuan Wang, Yunming Liao
Semi Supervised Model Training Split Federated Learning Robust Clustering Heterogeneous Participant

July 18, 2023

Robust Data Clustering with Outliers via Transformed Tensor Low-Rank Representation
Tong Wu
Individual Representation Tensor Data Tensor Based LOw Rank Robust Clustering Tensor Robust Principal Component Analysis Tensor Recovery Task SVD Framework

July 10, 2023

Robust Feature Learning Against Noisy Labels
Tsung-Ming Tai, Yun-Jie Jhang, Wen-Jyi Hwang
Supervised Learning Noisy Label Bootstrapping End to End Robust Clustering Robust Feature Robust Feature Representation

June 16, 2023

Adversarially robust clustering with optimality guarantees
Soham Jana, Kun Yang, Sanjeev Kulkarni
Adversarial Perturbation Gaussian Mixture Robust Clustering Optimality Guarantee

June 7, 2023

Effective Neural Topic Modeling with Embedding Clustering Regularization
Xiaobao Wu, Xinshuai Dong, Thong Nguyen, Anh Tuan Luu
Topic Model Neural Topic Model Topic Distribution Robust Clustering

May 12, 2023

Parameterized Approximation for Robust Clustering in Discrete Geometric Spaces
Fateme Abbasi, Sandip Banerjee, Jarosław Byrka, Parinya Chalermsook, Ameet Gadekar, Kamyar Khodamoradi, Dániel Marx, Roohani Sharma, Joachim Spoerhase
K$ Mean Robust Clustering Discrete Space K$ Center Parametric Approximation Disjoint Cluster High Dimensional Euclidean Space

February 28, 2023

Scalable Clustering: Large Scale Unsupervised Learning of Gaussian Mixture Models with Outliers
Yijia Zhou, Kyle A. Gallivan, Adrian Barbu
Gaussian Mixture Model Loss Minimization Robust Clustering

January 29, 2023

FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
Yongxin Guo, Xiaoying Tang, Tao Lin
Distribution Shift Robust Clustering Feature Distribution Shift

March 4, 2022

No More Than 6ft Apart: Robust K-Means via Radius Upper Bounds
Ahmed Imtiaz Humayun, Randall Balestriero, Anastasios Kyrillidis, Richard Baraniuk
Data Clustering K Mean Clustering Method Robust Clustering Feature Centroid Robustness Radius