dendextend (version 1.3.0)

Bk: Bk - Calculating Fowlkes-Mallows Index for two dendrogram

Description

Bk is the calculation of Fowlkes-Mallows index for a series of k cuts for two dendrograms.

Usage

Bk(tree1, tree2, k, include_EV = TRUE, warn = dendextend_options("warn"), ...)

Arguments

tree1

a dendrogram/hclust/phylo object.

tree2

a dendrogram/hclust/phylo object.

k

an integer scalar or vector with the desired number of cluster groups. If missing - the Bk will be calculated for a default k range of 2:(nleaves-1). No point in checking k=1/k=n, since both will give Bk=1.

include_EV

logical (TRUE). Should we calculate expectancy and variance of the FM Index under null hypothesis of no relation between the clusterings? If TRUE (Default) - then the FM_index_R function, else (FALSE) we use the (faster) FM_index_profdpm function.

warn

logical (default from dendextend_options("warn") is FALSE). Set if warning are to be issued, it is safer to keep this at TRUE, but for keeping the noise down, the default is FALSE.

...

Ignored (passed to FM_index_R/FM_index_profdpm).

Value

A list (of k's length) of Fowlkes-Mallows index between two dendrogram for a scalar/vector of k values. The names of the lists' items is the k for which it was calculated.

Details

From Wikipedia:

Fowlkes-Mallows index (see references) is an external evaluation method that is used to determine the similarity between two clusterings (clusters obtained after a clustering algorithm). This measure of similarity could be either between two hierarchical clusterings or a clustering and a benchmark classification. A higher the value for the Fowlkes-Mallows index indicates a greater similarity between the clusters and the benchmark classifications.

References

Fowlkes, E. B.; Mallows, C. L. (1 September 1983). "A Method for Comparing Two Hierarchical Clusterings". Journal of the American Statistical Association 78 (383): 553.

http://en.wikipedia.org/wiki/Fowlkes-Mallows_index

See Also

FM_index, cor_bakers_gamma, Bk_plot

Examples

## Not run: 
# 
# set.seed(23235)
# ss <- TRUE # sample(1:150, 10 )
# hc1 <- hclust(dist(iris[ss,-5]), "com")
# hc2 <- hclust(dist(iris[ss,-5]), "single")
# tree1 <- as.dendrogram(hc1)
# tree2 <- as.dendrogram(hc2)
# #    cutree(tree1)   
# 
# Bk(hc1, hc2, k = 3)
# Bk(hc1, hc2, k = 2:10)
# Bk(hc1, hc2)
# 
# Bk(tree1, tree2, k = 3)
# Bk(tree1, tree2, k = 2:5)
# 
# system.time(Bk(hc1, hc2, k = 2:5)) # 0.01
# system.time(Bk(hc1, hc2)) # 1.28
# system.time(Bk(tree1, tree2, k = 2:5)) # 0.24 # after fixes.
# system.time(Bk(tree1, tree2, k = 2:10)) # 0.31 # after fixes.
# system.time(Bk(tree1, tree2)) # 7.85 
# Bk(tree1, tree2, k= 99:101)
# 
# y <- Bk(hc1, hc2, k = 2:10)
# plot(unlist(y)~c(2:10), type = "b", ylim = c(0,1))
# 
# # can take a few seconds
# y <- Bk(hc1, hc2)
# plot(unlist(y)~as.numeric(names(y)), 
#      main = "Bk plot", pch = 20,
#      xlab = "k", ylab = "FM Index",
#      type = "b", ylim = c(0,1))
# # we are still missing some hypothesis testing here.
# # for this we'll have the Bk_plot function.
# 
# ## End(Not run)