Explainable Clustering

Explainable clustering aims to produce not only accurate data groupings but also readily understandable explanations for those groupings, addressing the critical need for transparency in high-stakes applications. Current research focuses on developing algorithms that integrate explainability directly into the clustering process, often using decision trees or prototype-based methods to generate human-interpretable descriptions of clusters, and incorporating privacy-preserving techniques. This field is crucial for building trust in AI systems and ensuring responsible use of clustering in domains like healthcare and finance, where understanding the basis of algorithmic decisions is paramount.

Papers

November 3, 2024

Decision Trees for Interpretable Clusters in Mixture Models and Deep Representations
Maximilian Fleissner, Maedeh Zarvandi, Debarghya Ghoshdastidar
Unsupervised Learning Decision Tree Meaningful Representation Mixture Model Explainable Machine Learning Explainable Clustering Interpretable Clustering

September 1, 2024

Interpretable Clustering: A Survey
Lianyu Hu, Mudi Jiang, Junjie Dong, Xinying Liu, Zengyou He
Timely Survey Practical Algorithm Inherent Interpretability Explainable Clustering Interpretable Clustering

June 7, 2024

Contrastive explainable clustering with differential privacy
Dung Nguyen, Ariel Vetzler, Sarit Kraus, Anil Vullikanti
Differential Privacy Differentially Private Contrastive Explanation Explainable Clustering

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

Explaining Kernel Clustering via Decision Trees
Maximilian Fleissner, Leena Chennuru Vankadara, Debarghya Ghoshdastidar
Decision Tree Interpretable Machine Learning Explainable Clustering Interpretable Clustering Multiple Kernel Clustering

February 5, 2024

Text-Guided Image Clustering
Andreas Stephan, Lukas Miklautz, Kevin Sidak, Jan Philip Wahle, Bela Gipp, Claudia Plant, Benjamin Roth
Text Representation Image Text Image Clustering Explainable Clustering

December 13, 2023

Prototypical Self-Explainable Models Without Re-training
Srishti Gautam, Ahcene Boubekki, Marina M. C. Höhne, Michael C. Kampffmeyer
Pre Trained Model Explainable AI Retraining Method Explainable Clustering Self Explaining Prototype Based Explanation

September 14, 2023

Massively-Parallel Heat Map Sorting and Applications To Explainable Clustering
Sepideh Aghamolaei, Mohammad Ghodsi
Financial Application K Mean Different Cluster Locality Sensitive Hashing Density Based Clustering Explainable Clustering

August 1, 2023

Explainable Graph Spectral Clustering of Text Documents
Bartłomiej Starosta, Mieczysław A. Kłopotek, Sławomir T. Wierzchoń
Data Clustering Spectral Clustering Clustering Result Explainable Clustering Text Document

July 16, 2023

Using Decision Trees for Interpretable Supervised Clustering
Natallia Kokash, Leonid Makhnist
Decision Tree Hierarchical Clustering Explainable Clustering Interpretable Clustering Conventional Clustering

May 23, 2023

Goal-Driven Explainable Clustering via Language Descriptions
Zihan Wang, Jingbo Shang, Ruiqi Zhong
Language Specific Goal Oriented Unsupervised Clustering Summarization System Explainable Clustering

May 4, 2023

Impossibility of Depth Reduction in Explainable Clustering
Chengyuan Deng, Surya Teja Gavva, Karthik C. S., Parth Patel, Adarsh Srinivasan
K Mean Explainable Clustering Optimal Clustering Depth Restoration

March 25, 2023

Shapley-based Explainable AI for Clustering Applications in Fault Diagnosis and Prognosis
Joseph Cohen, Xun Huan, Jun Ni
Explainable AI Smart Manufacturing Shapley Additive Explanation Prognosis Prediction Text Clustering Explainable Clustering

February 14, 2023

Multi-Prototypes Convex Merging Based K-Means Clustering Algorithm
Dong Li, Shuisheng Zhou, Tieyong Zeng, Raymond H. Chan
Practical Algorithm K Mean Explainable Clustering Prototype Classifier Class Agnostic Geometric Prototype

September 22, 2022

XClusters: Explainability-first Clustering
Hyunseung Hwang, Steven Euijong Whang
Decision Tree Data Clustering Clustering Result Explainable Clustering State of the Art Clustering

September 20, 2022

Explainable Clustering via Exemplars: Complexity and Efficient Approximation Algorithms
Ian Davidson, Michael Livanos, Antoine Gourru, Peter Walker, Julien Velcin, S. S. Ravi
Explainable AI Complexity Matter Feature Embeddings Exemplar Image Approximation Algorithm Explainable Clustering Low Complexity Explanation

August 20, 2022

The computational complexity of some explainable clustering problems
Eduardo Sany Laber
High Explainability Computational Complexity K$ Mean Explainable Clustering

February 3, 2022

Fast and explainable clustering based on sorting
Xinye Chen, Stefan Güttel
Different Cluster Explainable Clustering Clustering Performance Cluster Shape

December 29, 2021

Shallow decision trees for explainable $k$-means clustering
Eduardo Laber, Lucas Murtinho, Felipe Oliveira
Decision Tree K$ Mean Explainable Clustering

December 13, 2021

How to Find a Good Explanation for Clustering?
Sayan Bandyapadhyay, Fedor V. Fomin, Petr A. Golovach, William Lochet, Nidhi Purohit, Kirill Simonov
Data Clustering K$ Mean Explainable Clustering Quality Explanation