scholarly journals A Conformal Classifier for Dissimilarity Data

2012 ◽  
pp. 234-243 ◽  
Author(s):  
Frank-Michael Schleif ◽  
Xibin Zhu ◽  
Barbara Hammer
Keyword(s):  
Dissimilarity Data

PoClustering: Lossless Clustering of Dissimilarity Data

2007 ◽  
Author(s):  
Jinze Liu ◽  
Qi Zhang ◽  
Wei Wang ◽  
Leonard McMillan ◽  
Jan Prins
Keyword(s):  
Dissimilarity Data

scholarly journals Visual Encoding of Dissimilarity Data via Topology-Preserving Map Deformation

2016 ◽  
Vol 22 (9) ◽  
pp. 2200-2213 ◽  
Author(s):  
Quirijn W. Bouts ◽  
Tim Dwyer ◽  
Jason Dykes ◽  
Bettina Speckmann ◽  
Sarah Goodwin ◽  
...  
Keyword(s):  
Dissimilarity Data ◽  
Visual Encoding

Unfolding Models for Asymmetric Dissimilarity Data With External Information Based on Path Structures

2018 ◽  
Vol 6 (3) ◽  
pp. 53-66
Author(s):  
Kensuke Tanioka ◽  
Hiroshi Yadohisa
Keyword(s):  
Principal Component ◽  
External Information ◽  
Visualization Method ◽  
Low Dimensions ◽  
Dissimilarity Data ◽  
Asymmetric Relation ◽  
Loading Matrix ◽  
Unfolding Models ◽  
Asymmetric Multidimensional Scaling ◽  
Path Structure

This article contains asymmetric dissimilarity data which is observed in various situations. In asymmetric dissimilarity data, dissimilarity from subject i to j and from subject j to i are not the same necessarily. Asymmetric multidimensional scaling (AMDS) is a visualization method for describing the asymmetric relations between subjects, given asymmetric dissimilarity data for subjects. It is sure that AMDS is a useful tool for interpreting the asymmetric relation, however, existing AMDS cannot be considered for the external information, even if the external information of the same subjects for the asymmetric dissimilarity data is given. If the estimated coordinates can be interpreted from the loading matrix for the external information like principal component analysis (PCA), the AMDS become more useful. This is because we can interpret the relation between the estimated asymmetries and the factors of the external information on the low dimensions. In this article, we proposed new AMDS with external information. In addition to that, the proposed method can consider the path structure for variables like SEM.

scholarly journals k-CEVCLUS: Constrained evidential clustering of large dissimilarity data

2018 ◽  
Vol 142 ◽  
pp. 29-44 ◽  
Author(s):  
Feng Li ◽  
Shoumei Li ◽  
Thierry Denœux
Keyword(s):  
Dissimilarity Data

On the Construction of “Optimal” Phylogenetic Trees

1983 ◽  
Vol 38 (1-2) ◽  
pp. 156-158 ◽  
Author(s):  
Geert De Soete
Keyword(s):  
Phylogenetic Tree ◽  
Iterative Algorithm ◽  
Phylogenetic Trees ◽  
Data Set ◽  
Dissimilarity Data

An iterative algorithm for constructing the optimal phylogenetic tree from a given set o f dissimilarity data is described. The procedure is applied for illustrative purposes an a data set com piled by Fitch and Margoliash.

Diversity of Site-Specific Microbes of Occlusal and Proximal Lesions in Severe- Early Childhood Caries (S-ECC)

2021 ◽  
Author(s):  
Kausar Sadia Fakhruddin ◽  
Lakshman Perera Samaranayake ◽  
Rifat Akram Hamoudi ◽  
Hien Chi Ngo ◽  
Horoshi Egusa
Keyword(s):  
Early Childhood ◽  
Early Childhood Caries ◽  
Rrna Gene ◽  
Proximal Caries ◽  
Dissimilarity Data ◽  
Hypervariable Regions ◽  
Curtis Dissimilarity ◽  
Caries Lesions ◽  
Severe Early Childhood Caries ◽  
Childhood Caries

Abstract Background: Severe-early childhood caries (S-ECC) is a global problem of significant concern, commonly manifest as a dentinal lesion on the occlusal and proximal surfaces of the affected deciduous dentition. Although there are major ecological differences between these two niches, it is unclear whether these are reflected in the composition of their dysbiotic cariogenic microbiome. Therefore, we compared the compositional differences in the microbiota of occlusal and proximal caries lesions in S-ECC. Methods: Deep-dentine caries samples (19-occlusal and 19-proximal) from asymptomatic primary molars of children with S-ECC (n=19) belonging to caries-code 5/6, according to ICDAS classification, were evaluated. Employing two primer pools, we amplified and compared the bacterial 16S rRNA gene sequences of the seven hypervariable regions (V2 to V4 and V6 to V9) using a next-generation sequencing based assay.Results: Bray-Curtis dissimilarity data indicated that occlusal lesions had a more homogeneous microbial community structure than the proximal lesions with significant compositional differences at species level (p=0.01; R-value of 0.513). Together, the occlusal and proximal niches harbored 263 species, of which 202 (76.8%) species were common to both locales, while 49 (18.6%) and 12 (4.6%) disparate species were exclusively isolated from the proximal and occlusal niches, respectively. The most commonly found genera at both locales included Streptococcus, Prevotella, and Lactobacillus, with 33, 27, and 22 species each, respectively. In addition, Streptococcus mutans predominated in the proximal cavities (p≤0.05), as opposed to Atopobium parvulum (p=0.01) in the occlusal niches, while Vellonella alcalescens was present in similar proportions in both habitats (p≥0.05). Conclusions: Distinct differences between the caries microbiota of occlusal and proximal caries in S-ECC exist. The former niche appears to provide a habitat for a more homogeneous growth of communal microbiota than the latter. This may be due to the conditions prevalent in relatively quiescent inter-proximal regions, as opposed to the occlusal regions exposed to the ebb and flow of salivary and masticatory forces, and/or the anatomical and structural differences in the two locales. The clinical implications of these findings in terms of the rate and severity of caries progression remain to be determined.

Neural Gas Clustering for Dissimilarity Data with Continuous Prototypes

2007 ◽  
pp. 539-546 ◽  
Author(s):  
Alexander Hasenfuss ◽  
Barbara Hammer ◽  
Frank-Michael Schleif ◽  
Thomas Villmann
Keyword(s):  
Dissimilarity Data ◽  
Neural Gas
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