Interpretable Clustering

Interpretable clustering aims to group similar data points while providing understandable explanations for the resulting clusters, addressing the limitations of "black box" methods. Current research focuses on developing algorithms that integrate interpretability with clustering performance, employing techniques like decision trees, additive models, and constrained optimization to generate transparent and human-understandable cluster assignments. This field is crucial for building trust in AI systems across various domains, from healthcare and finance to autonomous systems, by ensuring that clustering-based decisions are justifiable and easily understood. The development of more interpretable clustering methods is driving progress in both the theoretical understanding of clustering and its practical application in data-driven decision-making.

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

October 7, 2024

Combining Structural and Unstructured Data: A Topic-based Finite Mixture Model for Insurance Claim Prediction
Yanxi Hou, Xiaolan Xia, Guangyuan Gao
Unstructured Data Topic Analysis Interpretable Clustering Probabilistic Topic Claim Frequency Claim Size Prediction

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

August 23, 2024

NeurCAM: Interpretable Neural Clustering via Additive Models
Nakul Upadhya, Eldan Cohen
Additive Model Neural Additive Model Interpretable Clustering

May 12, 2024

Human-interpretable clustering of short-text using large language models
Justin K. Miller, Tristram J. Alexander
Large Language Model Human Generated Short Text Interpretable Clustering Short Text Clustering

May 4, 2024

Interpretable Multi-View Clustering
Mudi Jiang, Lianyu Hu, Zengyou He, Zhikui Chen
Multi View Multi View Clustering Interpretable Clustering

April 24, 2024

Interpretable Clustering with the Distinguishability Criterion
Ali Turfah, Xiaoquan Wen
Hierarchical Clustering Cluster Analysis Clustering Result Interpretable Clustering Leave One Out Distinguishability

March 28, 2024

Maximum Likelihood Estimation on Stochastic Blockmodels for Directed Graph Clustering
Mihai Cucuringu, Xiaowen Dong, Ning Zhang
Spectral Clustering Maximum Likelihood Graph Clustering Block Model Interpretable Clustering Graph Structure Information

March 26, 2024

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

September 15, 2023

Automated dermatoscopic pattern discovery by clustering neural network output for human-computer interaction
Lidia Talavera-Martinez, Philipp Tschandl
Data Clustering Medical Image Datasets Human Machine Interaction Unsupervised Clustering Neural Network Output Interpretable Clustering

September 3, 2023

Interpretable Sequence Clustering
Junjie Dong, Xinyi Yang, Mudi Jiang, Lianyu Hu, Zengyou He
Decision Tree Sequential Pattern Interpretable Clustering Interpretable Tree Discriminative Pattern

July 16, 2023

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

June 7, 2023

Interpretable Deep Clustering for Tabular Data
Jonathan Svirsky, Ofir Lindenbaum
Feature Selection Tabular Data Interpretable Clustering Cluster Assignment

May 7, 2023

A Generalized Framework for Predictive Clustering and Optimization
Aravinth Chembu, Scott Sanner
Optimization Purpose Generalized Framework Interpretable Clustering Predictive Clustering Supervised 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

December 15, 2022

Variable Clustering via Distributionally Robust Nodewise Regression
Kaizheng Wang, Xiao Xu, Xun Yu Zhou
Cluster Analysis ADMM Algorithm Block Model Interpretable Clustering Node Level Regression

February 16, 2022

Spatial Transformer K-Means
Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan Sengupta, Richard Baraniuk, Behnaam Aazhang
K Mean Spatial Transformer Interpretable Clustering Centroid Based Clustering

December 10, 2021

Interpretable Clustering via Multi-Polytope Machines
Connor Lawless, Jayant Kalagnanam, Lam M. Nguyen, Dzung Phan, Chandra Reddy
Interpretable Clustering Subgroup Discovery Adversarial Polytope