LexCHCca: Chronological Constrained Hierarchical Clustering on...

In Xplortext: Statistical Analysis of Textual Data

Chronological Constrained Hierarchical Clustering on Correspondence Analysis Components (LexCHCca)

Description

Chronological constrained agglomerative hierarchical clustering on a corpus of documents

Usage

LexCHCca (object, nb.clust=0, min=2, max=NULL, nb.par=5, 
 graph=TRUE, proba=0.05, cut.test=FALSE, alpha.test =0.05, description=FALSE,
 nb.desc=5, size.desc=80)

Arguments

object	object of LexCA class
nb.clust	number of clusters only if no test (cut.test=FALSE). If 0 (or "click"), the tree is cut at the level the user clicks on. If -1 (or "auto"), the tree is automatically cut at the suggested level. If a (positive) integer, the tree is cut with nb.clust clusters (by default 0)
min	minimum number of clusters. Available only if cut.test=FALSE. (by default 3)
max	maximum number of clusters. Available only if cut.test=FALSE. (by default NULL; then max is computed as the minimum between 10 and the number of documents divided by 2)
nb.par	number of edited paragons (para) and specific documents labels (dist) (by default 5)
graph	if TRUE, graphs are displayed (by default TRUE)
proba	threshold on the p-value used to describe the clusters (by default 0.05)
cut.test	if FALSE (by default), Legendre test is not performed when joining two nodes. This test is used to determine whether two clusters should be joined or not; see details
alpha.test	threshold on the p-value used in selecting aggregation clusters for Legendre test (by default 0.05)
description	if TRUE, description of the clusters by the characteristic words/documents, paragon (para), specific documents (dist) and contextual variables if these latter have been selected in the previous LexCA function (by default FALSE)
nb.desc	number of paragons (para) and specific documents (dist) that are edited when describing the clusters (by default 5)
size.desc	maximum of characters when editing the paragons (para) and specific documents (dist) to describe the clusters (by default 80)

Details

LexCHCca starts from the document coordinates issued from a textual correspondence analysis. The hierarchical tree is built in such a way that only chronological contiguous nodes can be joined. The documents have to be ranked in their chronological order in the source-base (data frame format) before to apply the function (TextData format).

Legendre test allows to determine whether the fusion between two nodes based on their contiguity lead to a heterogenous new node (no homogeneity-between-clusters). If Legendre test is applied (cut.test=TRUE), the number of clusters is the number obtained by the test and nb.clust has not effects.

If no Legendre test is applied (cut.test= FALSE), the number of clusters is determined either a priori or from the constrained hierarchical tree structure.

The object $para contains the distance between each document and the centroid of its class.

The object $dist contains the distance between each document and the centroid of the farthest cluster.

The results of the description of the clusters and graphs are provided.

Value

Returns a list including:

data.clust	the active lexical table used in LexCA plus a new column called Clust_ containing the partition
coord.clust	coordinates table issued from CA plus a new column called weigths and another column called Clust_, corresponds to the partition
centers	coordinates of the gravity centers of the clusters
description	$des.word for description of the clusters of documents by their characteristic words, the paragons (des.doc$para) and specific documents (des.doc$dist) of each cluster; see details
call	list of internal objects. call$t giving the results for the hierarchical tree
dendro	hclust object. This allows for using the dendrogram in other packages
phases	details of the tracking of the agglomerative hierarchical process. In particular, the cut points (joining documents not allowed) can be identified
sum.squares	sum of squares decomposition for documents and clusters

Author(s)

Monica Bécue-Bertaut, Ramón Alvarez-Esteban [e-mail], Josep-Antón Sánchez-Espigares, Belchin Kostov

References

Bécue-Bertaut, M., Kostov, B., Morin, A., & Naro, G. (2014). Rhetorical Strategy in Forensic Speeches: Multidimensional Statistics-Based Methodology. Journal of Classification,31, 85-106. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00357-014-9148-9")}.

Husson F., Lê S., Pagès J. (2017). Exploratory Multivariate Analysis by Example Using R. Chapman & Hall/CRC. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1201/b21874")}.

Lebart L. (1978). Programme d'agrégation avec contraintes. Les Cahiers de l'Analyse des Données, 3, pp. 275–288.

Legendre, P. & Legendre, L. (1998), Numerical Ecology (2nd ed.), Amsterdam: Elsevier Science.

Murtagh F. (1985). Multidimensional Clustering Algorithms. Vienna: Physica-Verlag, COMPSTAT Lectures.

See Also

plot.LexCHCca, LexCA

Examples

data(open.question)
res.TD<-TextData(open.question,var.text=c(9,10), var.agg="Age_Group", Fmin=10, Dmin=10, 
        stop.word.tm=TRUE)
res.LexCA<-LexCA(res.TD, graph=FALSE)
res.ccah<-LexCHCca(res.LexCA, nb.clust=4, min=3)

Xplortext documentation built on Nov. 10, 2023, 1:06 a.m.