SPARTAAS (version 1.0.0)

hclustcompro: hclustcompro

Description

Compromised Hierarchical bottom-up clustering method. The method use two sources of informations. The merging of the two data sources is done by a parameter (alpha) which allows to weight each source. Formula: D_alpha = alpha * D1 + (1-alpha) * D2

Usage

hclustcompro(
  D1,
  D2,
  alpha="EstimateAlphaForMe",
  k=NULL,
  title="notitle",
  method="ward.D2"
)

Arguments

D1

First dissimilarity matrix (square matrix) or distance matrix. Could be a contingency table (see CAdist). A factorial correspondences analysis is carried out and the distances are used (Chi-square Metric).

D2

Second dissimilarity matrix (square matrix), same size than D1, or distance matrix.

alpha

The mixing parameter in order to generate the D_alpha matrix. Formula: D_alpha = alpha * D1 + (1-alpha) * D2

k

The number of cluster you want.

title

The title to display on the dendrogram plot.

method

The agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).

Value

The function returns a list (class: hclustcompro_cl).

D1

First dissimilarity matrix (square matrix)

D2

Second dissimilarity matrix (square matrix)

D_alpha

The matrix use in the CAH result of the mixing of the two matrix (D1 and D2)

alpha

Alpha

tree

An object of class hclust which describes the tree produced by the clustering process (see hclust)

cluster

The vector of cluster of the selected partition

cutree

Plot of the cut dendrogram

call

How you call the function

cont

Original contingency data (if D1 is a contingency table)

Details

CAH Data fusion (alpha optimal value parameter see hclustcompro_select_alpha). It is necessary to define the appropriate proportion for each data source. This is the first sensitive point of the method that the user must consider. A tool is provided to guide his decision.

Cut dendrogram The division into classes, and subclasses, is the second crucial point. It must be done on the basis of knowledge of the study area and some decision support tools such as the cluster silhouette or the calculation of intra cluster variability (WSS: Within Sum of Square). You can use hclustcompro_subdivide in order tu sub-divide a cluster into sub-cluster.

Examples

# NOT RUN {
library(SPARTAAS)

#network stratigraphic data (Network)
network <- data.frame(
  nodes = c("AI09","AI08","AI07","AI06","AI05","AI04","AI03",
  "AI02","AI01","AO05","AO04","AO03","AO02","AO01","APQR03","APQR02","APQR01"),
  edges = c("AI08,AI06","AI07","AI04","AI05","AI01","AI03","AI02","","","AO04","AO03",
  "AO02,AO01","","","APQR02","APQR01","")
)
#contingency table
cont <- data.frame(
  Cat10 = c(1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),
  Cat20 = c(4,8,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0),
  Cat30 = c(18,24,986,254,55,181,43,140,154,177,66,1,24,15,0,31,37),
  Cat40.50 = c(17,121,874,248,88,413,91,212,272,507,187,40,332,174,17,288,224),
  Cat60 = c(0,0,1,0,0,4,4,3,0,3,0,0,0,0,0,0,0),
  Cat70 = c(3,1,69,54,10,72,7,33,74,36,16,4,40,5,0,17,13),
  Cat80 = c(4,0,10,0,12,38,2,11,38,26,25,1,18,4,0,25,7),
  Cat100.101 = c(23,4,26,51,31,111,36,47,123,231,106,21,128,77,10,151,114),
  Cat102 = c(0,1,2,2,4,4,13,14,6,6,0,0,12,5,1,17,64),
  Cat110.111.113 = c(0,0,22,1,17,21,12,20,30,82,15,22,94,78,18,108,8),
  Cat120.121 = c(0,0,0,0,0,0,0,0,0,0,66,0,58,9,0,116,184),
  Cat122 = c(0,0,0,0,0,0,0,0,0,0,14,0,34,5,0,134,281),
  row.names = c("AI01","AI02","AI03","AI04","AO03","AI05","AO01","AI07","AI08",
  "AO02","AI06","AO04","APQR01","APQR02","AO05","APQR03","AI09")
)
#obtain the dissimilarities matrices
distance <- CAdist(cont, nPC = 11)
constraint <- adjacency(network)

#You can also run hclustcompro with the dist matrix directly
hclustcompro(D1 = distance, D2 = constraint, alpha = 0.7, k = 4)


# }