Cover Image

Interpretable Clustering Ensemble

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lv, Hang, Hu, Lianyu, Jiang, Mudi, Liu, Xinying, He, Zengyou
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Interpretable Clustering: A Survey
by: Hu, Lianyu, et al.
Published: (2024)

Interpretable Multi-View Clustering
by: Jiang, Mudi, et al.
Published: (2024)

Adversarial Fair Multi-View Clustering
by: Jiang, Mudi, et al.
Published: (2025)

Interpretable Fair Clustering
by: Jiang, Mudi, et al.
Published: (2025)

Clusterability test for categorical data
by: Hu, Lianyu, et al.
Published: (2023)

Two-cluster test
by: Liu, Xinying, et al.
Published: (2025)

Personalized Interpretable Classification
by: He, Zengyou, et al.
Published: (2023)

Conjunction Subspaces Test for Conformal and Selective Classification
by: He, Zengyou, et al.
Published: (2024)

Clusterability-Based Assessment of Potentially Noisy Views for Multi-View Clustering
by: Jiang, Mudi, et al.
Published: (2026)

Biological Sequence Clustering: A Survey
by: Zhang, Simeng, et al.
Published: (2026)

Federated One-Shot Ensemble Clustering
by: Duan, Rui, et al.
Published: (2024)

Learning Ensembles of Interpretable Simple Structure
by: Arwade, Gaurav, et al.
Published: (2025)

Inherently Interpretable Tree Ensemble Learning
by: Yang, Zebin, et al.
Published: (2024)

Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
by: Liu, Brian, et al.
Published: (2025)

Learning Accurate, Efficient, and Interpretable MLPs on Multiplex Graphs via Node-wise Multi-View Ensemble Distillation
by: Liu, Yunhui, et al.
Published: (2025)

k-HyperEdge Medoids for Clustering Ensemble
by: Li, Feijiang, et al.
Published: (2024)

Interpretable Credit Default Prediction with Ensemble Learning and SHAP
by: Yang, Shiqi, et al.
Published: (2025)

REALITrees: Rashomon Ensemble Active Learning for Interpretable Trees
by: Nguyen, Simon D., et al.
Published: (2026)

Structured Sparsity and Weight-adaptive Pruning for Memory and Compute efficient Whisper models
by: Mudi, Prasenjit K, et al.
Published: (2025)

Generalization Performance of Ensemble Clustering: From Theory to Algorithm
by: Zhang, Xu, et al.
Published: (2025)

Interpretable Deep Clustering for Tabular Data
by: Svirsky, Jonathan, et al.
Published: (2023)

Interpretable Clustering with the Distinguishability Criterion
by: Turfah, Ali, et al.
Published: (2024)

Image Clustering with External Guidance
by: Li, Yunfan, et al.
Published: (2023)

Interpretable Representation Learning for Additive Rule Ensembles
by: Behzadimanesh, Shahrzad, et al.
Published: (2025)

Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Data
by: Nji, Francis Ndikum, et al.
Published: (2024)

Feature Network Methods in Machine Learning and Applications
by: Mu, Xinying, et al.
Published: (2024)

Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles
by: Arrighi, Leonardo, et al.
Published: (2024)

SitPose: Real-Time Detection of Sitting Posture and Sedentary Behavior Using Ensemble Learning With Depth Sensor
by: Jin, Hang, et al.
Published: (2024)

Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering
by: Samad, Manar D., et al.
Published: (2026)

Performance-bounded Online Ensemble Learning Method Based on Multi-armed bandits and Its Applications in Real-time Safety Assessment
by: Hu, Songqiao, et al.
Published: (2025)

What makes an Ensemble (Un) Interpretable?
by: Bassan, Shahaf, et al.
Published: (2025)

Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects
by: Bargagli-Stoffi, Falco J., et al.
Published: (2020)

Parameter-Aware Ensemble SINDy for Interpretable Symbolic SGS Closure
by: Kang, Hanseul, et al.
Published: (2025)

BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis
by: Ni, Zelin, et al.
Published: (2023)

NeurCAM: Interpretable Neural Clustering via Additive Models
by: Upadhya, Nakul, et al.
Published: (2024)

EnsembleCI: Ensemble Learning for Carbon Intensity Forecasting
by: Yan, Leyi, et al.
Published: (2025)

From Static Structures to Ensembles: Studying and Harnessing Protein Structure Tokenization
by: Liu, Zijing, et al.
Published: (2025)

Bringing Clustering to MLL: Weakly-Supervised Clustering for Partial Multi-Label Learning
by: Chen, Yu, et al.
Published: (2026)

FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection
by: Lu, Xinying, et al.
Published: (2024)

Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning
by: Chen, Xiaolin, et al.
Published: (2021)