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Finding reproducible cluster partitions for the k-means algorithm

Finding reproducible cluster partitions for the k-means algorithm

http://www.wikidata.org/entity/Q34569865

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FogBank: a single cell segmentation across multiple cell lines and image modalities Pattern Recognition Analysis Reveals Unique Contrast Sensitivity Isocontours Using Static Perimetry Thresholds Across the Visual Field Network-Based Approaches to Explore Complex Biological Systems towards Network Medicine

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P2860

Finding reproducible cluster partitions for the k-means algorithm

http://www.wikidata.org/entity/Q34569865

description

2013 nî lūn-bûn

@nan

2013 թուականի Յունուարին հրատարակուած գիտական յօդուած

@hyw

2013 թվականի հունվարին հրատարակված գիտական հոդված

@hy

2013年の論文

@ja

2013年論文

@yue

2013年論文

@zh-hant

2013年論文

@zh-hk

2013年論文

@zh-mo

2013年論文

@zh-tw

2013年论文

@wuu

name

Finding reproducible cluster partitions for the k-means algorithm

@ast

Finding reproducible cluster partitions for the k-means algorithm

@en

Finding reproducible cluster partitions for the k-means algorithm

@nl

type

label

Finding reproducible cluster partitions for the k-means algorithm

@ast

Finding reproducible cluster partitions for the k-means algorithm

@en

Finding reproducible cluster partitions for the k-means algorithm

@nl

prefLabel

Finding reproducible cluster partitions for the k-means algorithm

@ast

Finding reproducible cluster partitions for the k-means algorithm

@en

Finding reproducible cluster partitions for the k-means algorithm

@nl

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1471-2105-14-S1-S8

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BMC Bioinformatics

P1476

Finding reproducible cluster partitions for the k-means algorithm

@en

P2093

Ian H Jarman

http://www.w3.org/2001/XMLSchema#string

Paulo J G Lisboa

http://www.w3.org/2001/XMLSchema#string

Simon J Chambers

http://www.w3.org/2001/XMLSchema#string

Terence A Etchells

http://www.w3.org/2001/XMLSchema#string

P2860

A stability based method for discovering structure in clustered data.

Penalized and weighted K-means for clustering with scattered objects and prior information in high-throughput biological data.

Optimized data fusion for K-means Laplacian clustering.

Survey of clustering algorithms.

High-throughput protein expression analysis using tissue microarray technology of a large well-characterised series identifies biologically distinct classes of breast cancer confirming recent cDNA expression analyses.

K-means clustering: a half-century synthesis.

Using hybrid hierarchical K-means (HHK) clustering algorithm for protein sequence motif super-rule-tree (SRT) structure construction.

Stability analysis in K-means clustering.

Evaluation of stability of k-means cluster ensembles with respect to random initialization.

Profiling local optima in K-means clustering: developing a diagnostic technique.

P2888

1471-2105-14-s1-s8

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S8

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scholarly article

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10.1186/1471-2105-14-S1-S8

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P478

14 Suppl 1

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2013-01-14T00:00:00Z

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257870082

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1017556718

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P698

23369085

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P921

reproducibility

P932

3548705

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