Papers Deep Clustering
“Deep Clustering” 태그가 달린 논문 343편 · 필터 해제
Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data
Data imbalance poses a major challenge in supervised classification, where the majority-class bias contributes to false negatives and overestimates classification accuracy. Unsupervised deep clustering can be immune to c…
Representation LearningDeep ClusteringFMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series
In many realistic scenarios, large volumes of time series data are generated with limited or expensive annotations. This limitation makes supervised learning methods difficult to apply and leads to the use of unsupervise…
Self-Supervised LearningDeep ClusteringExpert-Driven Survival Machines: Improving Stratification and Interpretability in Multiple Clinical Cohorts
Survival prediction plays a central role for healthcare providers and clinical researchers. Accurate risk stratification enables early intervention and improved patient management. Most existing deep survival models lear…
Deep ClusteringUniFair: A unified fair clustering approach based on separation and compactness
Clustering is increasingly used to support high-impact decisions, yet standard objectives such as k-means can produce clusterings that treat demographic groups unequally. Existing fair clustering methods typically optimi…
Deep ClusteringCLUBench: A Clustering Benchmark
Clustering is a fundamental problem in data science with a long-standing research history, yielding numerous insightful algorithms. Despite this progress, a systematic and large-scale empirical evaluation that jointly co…
Text ClusteringDeep ClusteringInformation theoretic underpinning of self-supervised learning by clustering
Self-supervised learning (SSL) is recognized as an essential tool for building foundation models for Artificial Intelligence applications. The advances in SSL have been made thanks to vigorous arguments about the princip…
Self-Supervised LearningDeep ClusteringDeep Clustering for Climate: Analyzing Teleconnections through Learned Categorical States
Understanding and representing complex climate variability is essential for both scientific analysis and predictive modeling. However, identifying meaningful climate regimes from raw variables is challenging, as they exh…
Deep ClusteringBeyond Statistical Co-occurrence: Unlocking Intrinsic Semantics for Tabular Data Clustering
Deep Clustering (DC) has emerged as a powerful tool for tabular data analysis in real-world domains like finance and healthcare. However, most existing methods rely on data-level statistical co-occurrence to infer the la…
Contrastive LearningDeep ClusteringMining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering
In electronic health records (EHRs), clustering patients and distinguishing disease subtypes are key tasks to elucidate pathophysiology and aid clinical decision-making. However, clustering in healthcare informatics is s…
Image ClusteringDeep ClusteringDDCL: Deep Dual Competitive Learning: A Differentiable End-to-End Framework for Unsupervised Prototype-Based Representation Learning
A persistent structural weakness in deep clustering is the disconnect between feature learning and cluster assignment. Most architectures invoke an external clustering step, typically k-means, to produce pseudo-labels th…
Representation LearningDeep ClusteringDeep Image Clustering Based on Curriculum Learning and Density Information
Image clustering is one of the crucial techniques in multimedia analytics and knowledge discovery. Recently, the Deep clustering method (DC), characterized by its ability to perform feature learning and cluster assignmen…
Image ClusteringDeep Clusteringi-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data
Unsupervised learning of high-dimensional data is challenging due to irrelevant or noisy features obscuring underlying structures. It's common that only a few features, called the influential features, meaningfully defin…
Deep ClusteringTDEC: Deep Embedded Image Clustering with Transformer and Distribution Information
Image clustering is a crucial but challenging task in multimedia machine learning. Recently the combination of clustering with deep learning has achieved promising performance against conventional methods on high-dimensi…
Image ClusteringDeep ClusteringDeep Clustering based Boundary-Decoder Net for Inter and Intra Layer Stress Prediction of Heterogeneous Integrated IC Chip
High stress occurs when 3D heterogeneous IC packages are subjected to thermal cycling at extreme temperatures. Stress mainly occurs at the interface between different materials. We investigate stress image using latent s…
Deep ClusteringKhatri-Rao Clustering for Data Summarization
As datasets continue to grow in size and complexity, finding succinct yet accurate data summaries poses a key challenge. Centroid-based clustering, a widely adopted approach to address this challenge, finds informative s…
Representation LearningDeep ClusteringHow to Achieve the Intended Aim of Deep Clustering Now, without Deep Learning
Deep clustering (DC) is often quoted to have a key advantage over $k$-means clustering. Yet, this advantage is often demonstrated using image datasets only, and it is unclear whether it addresses the fundamental limitati…
Deep ClusteringNPCNet: Navigator-Driven Pseudo Text for Deep Clustering of Early Sepsis Phenotyping
Electronic Health Records (EHRs) provide high-dimensional temporal data essential for patient modeling; however, conventional algorithmic approaches often rely on data aggregation or imputation, which distorts temporal d…
Clinical KnowledgeDeep ClusteringDeep Variational Contrastive Learning for Joint Risk Stratification and Time-to-Event Estimation
Survival analysis is essential for clinical decision-making, as it allows practitioners to estimate time-to-event outcomes, stratify patient risk profiles, and guide treatment planning. Deep learning has revolutionized t…
Contrastive LearningDeep ClusteringClustering High-dimensional Data: Balancing Abstraction and Representation Tutorial at AAAI 2026
How to find a natural grouping of a large real data set? Clustering requires a balance between abstraction and representation. To identify clusters, we need to abstract from superfluous details of individual objects. But…
Representation LearningDeep ClusteringMulti-channel multi-speaker transformer for speech recognition
With the development of teleconferencing and in-vehicle voice assistants, far-field multi-speaker speech recognition has become a hot research topic. Recently, a multi-channel transformer (MCT) has been proposed, which d…
Speech RecognitionDeep Clustering