Publications associées à Fairness and Explainability in Clustering Problems

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Fast K-Means with Accurate Bounds

François Fleuret, James Peter Newling

We propose a novel accelerated exact

k

-means algorithm, which outperforms the current state-of-the-art low-dimensional algorithm in 18 of 22 experiments, running up to 3

\times

faster. We also propose a general improvement of existing state-of-the-art a ...

Idiap2016

Fairness and Explainability in Clustering Problems

Graph Chatbot

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A Bi-Criteria Approximation Algorithm for k-Means

Clustering of heterogeneous networks with directional flows based on “Snake” similarities

INFORMATION THEORETIC CLUSTERING FOR UNSUPERVISED DOMAIN-ADAPTATION

INFORMATION THEORETIC CLUSTERING FOR UNSUPERVISED DOMAIN-ADAPTATION

APPROXIMATING k-MEDIAN VIA PSEUDO-APPROXIMATION

Pose and Pathosformel in Aby Warburg’s Bilderatlas

Fast K-Means with Accurate Bounds

Centrality of trees for capacitated -center

Cluster Validity Measure and Merging System for Hierarchical Clustering considering Outliers

Nmf-Based Blind Source Separation Using A Linear Predictive Coding Error Clustering Criterion

A Bi-Criteria Approximation Algorithm for k-Means

Clustering of heterogeneous networks with directional flows based on “Snake” similarities

INFORMATION THEORETIC CLUSTERING FOR UNSUPERVISED DOMAIN-ADAPTATION

INFORMATION THEORETIC CLUSTERING FOR UNSUPERVISED DOMAIN-ADAPTATION

Pose and Pathosformel in Aby Warburg’s Bilderatlas

Nmf-Based Blind Source Separation Using A Linear Predictive Coding Error Clustering Criterion

Fast K-Means with Accurate Bounds

Cluster Validity Measure and Merging System for Hierarchical Clustering considering Outliers

APPROXIMATING k-MEDIAN VIA PSEUDO-APPROXIMATION

Centrality of trees for capacitated -center