scholarly journals The Chromosomal and Functional Clustering of Markedly Divergent Human-Mouse Orthologs Run Parallel to their Compositional Features

2016 ◽  
Vol 1 (1) ◽  
pp. 31-49 ◽  
Author(s):  
Miguel A. Fuertes  ◽  
José R. Rodrigo  ◽  
Emile Zuckerkandl  ◽  
Carlos   Alonso
Keyword(s):  
Functional Clustering ◽  
Mouse Orthologs

Posterior simulation across nonparametric models for functional clustering

Sankhya B ◽  
2011 ◽  
Vol 73 (1) ◽  
pp. 42-61 ◽  
Author(s):  
Jamie L. Crandell ◽  
David B. Dunson
Keyword(s):  
Functional Clustering ◽  
Nonparametric Models ◽  
Posterior Simulation

Multivariate functional clustering and its application to typhoon data

2018 ◽  
Vol 46 (1) ◽  
pp. 163-175 ◽  
Author(s):  
Toshihiro Misumi ◽  
Hidetoshi Matsui ◽  
Sadanori Konishi
Keyword(s):  
Functional Clustering

scholarly journals Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering

2018 ◽  
Vol 8 (10) ◽  
pp. 1766 ◽  
Author(s):  
Arthur Leroy ◽  
Andy MARC ◽  
Olivier DUPAS ◽  
Jean Lionel REY ◽  
Servane Gey
Keyword(s):  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Clustering Algorithms ◽  
Principal Component ◽  
Continuous Functions ◽  
Functional Principal Component Analysis ◽  
Data Set ◽  
Functional Clustering ◽  
Sport Science

Many data collected in sport science come from time dependent phenomenon. This article focuses on Functional Data Analysis (FDA), which study longitudinal data by modelling them as continuous functions. After a brief review of several FDA methods, some useful practical tools such as Functional Principal Component Analysis (FPCA) or functional clustering algorithms are presented and compared on simulated data. Finally, the problem of the detection of promising young swimmers is addressed through a curve clustering procedure on a real data set of performance progression curves. This study reveals that the fastest improvement of young swimmers generally appears before 16 years old. Moreover, several patterns of improvement are identified and the functional clustering procedure provides a useful detection tool.

scholarly journals ECOLOGICAL AND FUNCTIONAL CLUSTERING OF FORESTAL SPECIES IN THE SOUTH WESTERN AMAZONIA

2016 ◽  
Vol 40 (6) ◽  
pp. 1083-1090
Author(s):  
Elias Lourenço Vasconcelos Neto ◽  
Celso Azevedo ◽  
Luciano Ribas ◽  
Marcus Neves d'Oliveira
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Tree Species ◽  
Permanent Plots ◽  
Cumulative Frequency ◽  
Cumulative Frequency Distribution ◽  
Functional Clustering ◽  
Breast Height ◽  
Three Stages ◽  
Species Grouping

ABSTRACT The aim of this study was to perform ecological and functional clustering of tree species in southwestern Amazon. Developed from data from 95 permanent plots of 1 ha each, all individuals with diameter at breast height (DBH) ≥10 cm were measured. The species grouping was performed in three stages: (1) cluster analysis, using the variables: diameter annual periodic increment -(IPADAP) considering three competition levels (high, medium and low) and the 95th percentile of the diameters (P95) cumulative frequency distribution (Ward hierarchical method); (2) Discriminant analysis, using the variables P95 and IPADAP by Fisher's method and (3) subjective stage, considering the species ecological characteristics. The Ward and Fisher methods used for discriminant and cluster analyses were effective for species grouping resulting on the formation of 10 groups. Variables: IPADAP and and P95 were efficient on the formed groups discrimination.. Variations in the growth rates for the overall mean data were reduced wen calculated for each group of species.

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