Last updated: 2024-08-16

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File Version Author Date Message
Rmd 5bf3bfd Dave Tang 2024-08-16 Pros and cons of different linkage methods
html 850c62d Dave Tang 2024-08-16 Build site.
Rmd ad9ead7 Dave Tang 2024-08-16 Hierarchical clustering

Hierarchical clustering is a bottom-up approach, by which similar observations and sub-classes are assembled iteratively. The order of the labels does not matter within sibling pairs. Horizontal distances are usually meaningless, while the vertical distances do encode some information. These properties are important to remember when making interpretations about neighbours that are not monophyletic (i.e., not in the same subtree or clade), but appear as neighbours in the plot.

An alternative, top-down, approach takes all the objects and splits them sequentially according to a chosen criterion. Such so-called recursive partitioning methods are often used to make decision trees. They can be useful for prediction: the goal is to split heterogeneous populations into more homogeneous subgroups by partitioning.

Computing (dis)dimilarities between aggregates

There are different choices of how to calculate distances between aggregates and each choice results in a different type of hierarchical clustering.

Method Pros Cons
Single linkage Number of clusters Comb-like trees
Complete linkage Compact classes One observation can alter groups
Average linkage Similar size and variance Not robust
Centroid Robust to outliers Smaller number of clusters
Ward’s Minimises inertia Classes small if high variability

sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
 [5] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
 [9] ggplot2_3.5.1   tidyverse_2.0.0 workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] sass_0.4.9        utf8_1.2.4        generics_0.1.3    stringi_1.8.4    
 [5] hms_1.1.3         digest_0.6.35     magrittr_2.0.3    timechange_0.3.0 
 [9] evaluate_0.24.0   grid_4.4.0        fastmap_1.2.0     rprojroot_2.0.4  
[13] jsonlite_1.8.8    processx_3.8.4    whisker_0.4.1     ps_1.7.6         
[17] promises_1.3.0    httr_1.4.7        fansi_1.0.6       scales_1.3.0     
[21] jquerylib_0.1.4   cli_3.6.2         rlang_1.1.4       munsell_0.5.1    
[25] withr_3.0.0       cachem_1.1.0      yaml_2.3.8        tools_4.4.0      
[29] tzdb_0.4.0        colorspace_2.1-0  httpuv_1.6.15     vctrs_0.6.5      
[33] R6_2.5.1          lifecycle_1.0.4   git2r_0.33.0      fs_1.6.4         
[37] pkgconfig_2.0.3   callr_3.7.6       pillar_1.9.0      bslib_0.7.0      
[41] later_1.3.2       gtable_0.3.5      glue_1.7.0        Rcpp_1.0.12      
[45] xfun_0.44         tidyselect_1.2.1  rstudioapi_0.16.0 knitr_1.47       
[49] htmltools_0.5.8.1 rmarkdown_2.27    compiler_4.4.0    getPass_0.2-4