#clustering

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16 papers match

stat.ML2026

Cross-Cluster Weighted Forests

Maya Ramchandran, Rajarshi Mukherjee, Giovanni Parmigiani

The paper introduces Cross-Cluster Weighted Forests, an ensemble method that clusters training data, fits a random forest within each cluster, and combines them using stacked regre…

#ensemble methods#random forest#clustering#heterogeneous data
cs.LG2026

Counterfactuals for Feature-Weighted Clustering

Richard J. Fawley, Renato Cordeiro de Amorim

The paper proposes VoICE, a framework that generates counterfactual explanations for feature‑weighted k‑means clustering by projecting points onto weighted Voronoi regions of targe…

#counterfactual explanations#clustering#feature-weighted k-means#voronoi regions
stat.ME2026

Clustering of multivariate tail dependence using conditional methods

Patrick O'Toole, Christian Rohrbeck, Jordan Richards

The paper introduces a clustering method for multivariate extreme values that uses a new closed‑form dissimilarity measure based on the skew‑geometric Jensen‑Shannon divergence wit…

#extreme value theory#multivariate tail dependence#clustering#conditional extremes
eess.SP2026

Quantifying the complexity of trajectory ensembles with clustering-weighted multivariate multiscale sample entropy

Chenxiao Tian, J/"urgen Hackl

The paper proposes a new measure, clustering-weighted multivariate multiscale sample entropy (CWMMSE), that quantifies both the dynamical complexity of individual trajectories and…

#entropy#trajectory analysis#multiscale methods#clustering
cs.LG2026

FastCentNN: Accelerating Centroid Neural Network with Entropy Proxy

Le-Anh Tran

The paper introduces FastCentNN, an accelerated version of the Centroid Neural Network that uses an early splitting strategy based on a training entropy proxy to reduce unnecessary…

#unsupervised learning#clustering#online learning#algorithm acceleration
cs.IR2026

Cluster with Auctions for Vector Search

Swann Bessa, Pierre Fernandez, Gergely Szilvasy +2

The paper introduces CwA, a method that jointly learns a balanced clustering of database vectors and a neural probing function for large‑scale vector search, using a parallelizable…

#approximate nearest neighbor#vector indexing#clustering#neural probing