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Clustering ensemble selection considering quality and diversity

https://doi.org/10.1007/s10462-018-9642-2

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Resampling as a cluster validation technique in fMRI

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Cluster ensembles

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Stability-Based Model Order Selection in Clustering with Applications to Gene Expression Data

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Cluster Validity Using Support Vector Machines

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Cluster Ensemble Methods: from Single Clusterings to Combined Solutions

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A New Criterion for Clusters Validation

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Selective Clustering Ensemble Based on Covariance

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Projective clustering ensembles

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Cluster ensemble selection based on relative validity indexes

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Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy

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Kernelized Cluster Validity Measures and Application to Evaluation of Different Clustering Algorithms

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Enhancing Single-Objective Projective Clustering Ensembles

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Weighted Spectral Cluster Ensemble

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Learning Pairwise Similarity for Data Clustering

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inferredcites →https://doi.org/10.1137/1.9781611972788.71openalex-snapshot

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Spectral Ensemble Clustering

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Resampling Method for Unsupervised Estimation of Cluster Validity

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Stability-Based Validation of Clustering Solutions

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Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms

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inferred← citeshttps://doi.org/10.64960/easr.2026.262497openalex-snapshot

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