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MultiClust special issue on discovering, summarizing and using multiple clusterings

Machine learning, 2015-01, Vol.98 (1-2), p.1-5

2015

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Unsupervised feature selection with ensemble learning

Machine learning, 2015-01, Vol.98 (1-2), p.157-180

2015

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The blind men and the elephant: on meeting the problem of multiple truths in data from clustering and pattern mining perspectives

Machine learning, 2015-01, Vol.98 (1-2), p.121-155

2015

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Comparative study of matrix refinement approaches for ensemble clustering

Machine learning, 2015-01, Vol.98 (1-2), p.269-300

2015

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Probabilistic consensus clustering using evidence accumulation

Machine learning, 2015-01, Vol.98 (1-2), p.331-357

2015

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Greedy learning of latent tree models for multidimensional clustering

Machine learning, 2015-01, Vol.98 (1-2), p.301-330

2015

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A framework to uncover multiple alternative clusterings

Machine learning, 2015-01, Vol.98 (1-2), p.7-30

2015

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Metacluster-based Projective Clustering Ensembles

Machine learning, 2015-01, Vol.98 (1-2), p.181-216

2015

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Subjectively interesting alternative clusterings

Machine learning, 2015-01, Vol.98 (1-2), p.31-56

2015

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Unsupervised ensemble minority clustering

Machine learning, 2015-01, Vol.98 (1-2), p.217-268

2015

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A flexible cluster-oriented alternative clustering algorithm for choosing from the Pareto front of solutions

Machine learning, 2015-01, Vol.98 (1-2), p.57-91

2015

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Enriched spatial comparison of clusterings through discovery of deviating subspaces

Machine learning, 2015-01, Vol.98 (1-2), p.93-120

2015

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MultiClust special issue on discovering, summarizing and using multiple clusterings

Unsupervised feature selection with ensemble learning

The blind men and the elephant: on meeting the problem of multiple truths in data from clustering and pattern mining perspectives

Comparative study of matrix refinement approaches for ensemble clustering

Probabilistic consensus clustering using evidence accumulation

Greedy learning of latent tree models for multidimensional clustering

A framework to uncover multiple alternative clusterings

Metacluster-based Projective Clustering Ensembles

Subjectively interesting alternative clusterings

Unsupervised ensemble minority clustering

A flexible cluster-oriented alternative clustering algorithm for choosing from the Pareto front of solutions

Enriched spatial comparison of clusterings through discovery of deviating subspaces

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