Constrained Clustering

Constrained clustering enhances traditional clustering methods by incorporating prior knowledge, typically in the form of pairwise constraints (must-link and cannot-link relationships between data points), to guide the clustering process and improve results. Current research focuses on developing efficient algorithms, including those based on constraint programming, reinforcement learning, and spectral clustering, to handle large-scale datasets and various constraint types, such as cluster-level capacity constraints. This field is significant because it allows for the integration of expert knowledge into unsupervised learning, leading to more accurate, interpretable, and applicable clustering solutions across diverse domains, from image analysis to resource allocation.

Papers

March 26, 2024

Towards Explainable Clustering: A Constrained Declarative based Approach
Mathieu Guilbert, Christel Vrain, Thi-Bich-Hanh Dao
Constructive Approach Data Clustering Explainable Clustering Interpretable Clustering Constrained Clustering Declarative Statement

February 15, 2024

Large Scale Constrained Clustering With Reinforcement Learning
Benedikt Schesch, Marco Caserta
Reinforcement Learning Different Cluster Specific Heuristic Constrained Clustering Combinatorial Optimization Solver Clustering Problem

December 7, 2023

Constrained Hierarchical Clustering via Graph Coarsening and Optimal Cuts
Eliabelle Mauduit, Andrea Simonetto
Graph Coarsening Constrained Clustering Structural Constraint Cut Problem New Event Cluster External Constraint Graph Sequence

November 26, 2023

ConstraintMatch for Semi-constrained Clustering
Jann Goschenhofer, Bernd Bischl, Zsolt Kira
Self Supervised Semi Supervised Learning Constrained Learning Constrained Clustering Constraint Compliance Label Constraint

October 18, 2023

Improving Representation Learning for Histopathologic Images with Cluster Constraints
Weiyi Wu, Chongyang Gao, Joseph DiPalma, Soroush Vosoughi, Saeed Hassanpour
Self Supervised Learning Representation Learning Histopathological Image Slide Level Constrained Clustering

May 30, 2023

Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach
Tri Nguyen, Shahana Ibrahim, Xiao Fu
Data Clustering Deep Clustering Noisy Annotation Geometric Regularization Constrained Clustering Deep Multiple Clustering

February 28, 2023

Semi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directions
Germán González-Almagro, Daniel Peralta, Eli De Poorter, José-Ramón Cano, Salvador García
Clustering Method Research Direction Unsupervised Machine Learning Constrained Clustering Pairwise Constraint

February 10, 2023

Neural Capacitated Clustering
Jonas K. Falkner, Lars Schmidt-Thieme
Dynamic Routing Constrained Clustering Neural Clustering

January 30, 2023

Optimal Decision Trees For Interpretable Clustering with Constraints (Extended Version)
Pouya Shati, Eldan Cohen, Sheila McIlraith
Decision Tree Participation Constraint Extended Version Interpretable Clustering Constrained Clustering

October 14, 2022

Spatiotemporal Classification with limited labels using Constrained Clustering for large datasets
Praveen Ravirathinam, Rahul Ghosh, Ke Wang, Keyang Xuan, Ankush Khandelwal, Hilary Dugan, Paul Hanson, Vipin Kumar
Representation Learning Spatiotemporal Representation Large Datasets Spatio Temporal Learning Temporal Representation Limited Label Constrained Clustering Separable Representation Spatiotemporal Clustering

September 22, 2022

A Bibliographic View on Constrained Clustering
Ludmila Kuncheva, Francis Williams, Samuel Hennessey
Active Learning Ensemble Learning Automatic Analysis Constrained Clustering Large Scale Comparison Bibliographic Similarity

March 23, 2022

Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation
Benedikt Boecking, Vincent Jeanselme, Artur Dubrawski
Clustering Algorithm Distance Metric Logical Constraint Constrained Clustering Kernel Learning Constraint Relaxation Pairwise Constraint

March 7, 2022

State space partitioning based on constrained spectral clustering for block particle filtering
Rui Min, Christelle Garnier, François Septier, John Klein
State Space Spectral Clustering Particle Filter Constrained Clustering pFL Method State Vector

March 3, 2022

On consistency of constrained spectral clustering under representation-aware stochastic block model
Shubham Gupta, Ambedkar Dukkipati
Strong Consistency Spectral Clustering Stochastic Block Model Constrained Clustering

December 17, 2021

Semi-Supervised Clustering via Information-Theoretic Markov Chain Aggregation
Sophie Steger, Bernhard C. Geiger, Marek Smieja
Unsupervised Clustering Semi Supervised Clustering Constrained Clustering Q Aggregation

November 30, 2021

An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clustering
Veronica Piccialli, Anna Russo Russo, Antonio M. Sudoso
Data Clustering K Mean Exact Algorithm Constrained Clustering Minimum Sum of Square Clustering

Constrained Clustering

Papers

Towards Explainable Clustering: A Constrained Declarative based Approach

Large Scale Constrained Clustering With Reinforcement Learning

Constrained Hierarchical Clustering via Graph Coarsening and Optimal Cuts

ConstraintMatch for Semi-constrained Clustering

Improving Representation Learning for Histopathologic Images with Cluster Constraints

Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach

Semi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directions

Neural Capacitated Clustering

Optimal Decision Trees For Interpretable Clustering with Constraints (Extended Version)

Spatiotemporal Classification with limited labels using Constrained Clustering for large datasets

A Bibliographic View on Constrained Clustering

Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation

State space partitioning based on constrained spectral clustering for block particle filtering

On consistency of constrained spectral clustering under representation-aware stochastic block model

Semi-Supervised Clustering via Information-Theoretic Markov Chain Aggregation

An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clustering