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Journal ArticleDOI

A K-Means Clustering Algorithm

J. A. Hartigan, +1 more
- 01 Mar 1979 - 
- Vol. 28, Iss: 1, pp 100-108
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This article is published in Journal of The Royal Statistical Society Series C-applied Statistics.The article was published on 1979-03-01. It has received 10702 citations till now. The article focuses on the topics: Canopy clustering algorithm & Correlation clustering.

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Journal ArticleDOI

Relatedness and Genotype × Environment Interaction Affect Prediction Accuracies in Genomic Selection: A Study in Cassava

TL;DR: A method to assess effects of cross-validation sampling schemes preventing the training and validation sets from sharing genetically close clones or similar evaluation locations in cassava and found that predicting for less related clones decreased accuracy, a small but consistent effect.
Book ChapterDOI

Restoring ink bleed-through degraded document images using a recursive unsupervised classification technique

TL;DR: This paper presents a new method to restore a particular type of degradation related to ancient document images referred to as “bleed-through”, based on a recursive unsupervised segmentation approach applied on the decorrelated data space by the principal component analysis.
Journal ArticleDOI

CODE: Coherence Based Decision Boundaries for Feature Correspondence

TL;DR: A non-linear regression technique is proposed that can discover a coherence based separability constraint from highly noisy matches and embed it into a correspondence likelihood model and integrate the model into a full feature correspondence system which reliably generates large numbers of good quality correspondences over wide baselines.
Journal ArticleDOI

Integrated Network Analysis Identifies Fight-Club Nodes as a Class of Hubs Encompassing Key Putative Switch Genes That Induce Major Transcriptome Reprogramming during Grapevine Development

TL;DR: The identification of known master regulators of tomato fruit maturation suggests the method is suitable for the detection of key regulators of organ development in different fleshy fruit crops.
Book ChapterDOI

A Survey of Partitional and Hierarchical Clustering Algorithms

TL;DR: The two most widely studied clustering algorithms are partitional and hierarchical clustering as discussed by the authors, which can be achieved in two different ways, namely, bottom-up and top-down clustering.
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