Performs an agglomerative hierarchical clustering on results from a factor analysis. It is possible to cut the tree by clicking at the suggested (or an other) level. Results include paragons, description of the clusters, graphics.
HCPC(res, nb.clust=0, consol=TRUE, iter.max=10, min=3, max=NULL, metric="euclidean", method="ward", order=TRUE, graph.scale="inertia", nb.par=5, graph=TRUE, proba=0.05, cluster.CA="rows",kk=Inf,...)
- Either the result of a factor analysis, a dataframe, or a vector.
- an integer. If 0, the tree is cut at the level the user clicks on. If -1, the tree is automatically cut at the suggested level (see details). If a (positive) integer, the tree is cut with nb.cluters clusters.
- a boolean. If TRUE, a k-means consolidation is performed.
- An integer. The maximum number of iterations for the consolidation.
- an integer. The least possible number of clusters suggested.
- an integer. The higher possible number of clusters suggested; by default the minimum between 10 and the number of individuals divided by 2.
- The metric used to built the tree. See
- The method used to built the tree. See
- A boolean. If TRUE, clusters are ordered following their center coordinate on the first axis.
- A character string. By default "inertia" and the height of the tree corresponds to the inertia gain, else "sqrt-inertia" the square root of the inertia gain.
- An integer. The number of edited paragons.
- If TRUE, graphics are displayed. If FALSE, no graph are displayed.
- The probability used to select axes and variables in catdes (see
- A string equals to "rows" or "columns" for the clustering of Correspondence Analysis results.
- An integer; if this integer is greater than 0, a Kmeans is performed with kk clusters and the top of the hierarchical tree is constructed from this partition. This is very useful if the number of individuals is high. Inf can be used to construct all the tree.
- Other arguments from other methods.
The function first built a hierarchical tree. Then the sum of the within-cluster inertia are calculated for each partition. The suggested partition is the one with the higher relative loss of inertia (i(clusters n+1)/i(cluster n)). The absolut loss of inertia (i(cluster n)-i(cluster n+1)) is ploted with the tree. If the ascending clustering is constructed from a data-frame with a lot of rows (individuals), it is possible to first perform a partition with kk clusters and then construct the tree from the (weighted) kk clusters.
Returns a list including: Returns the tree and a barplot of the inertia gains, the individual factor map with the tree (3D), the factor map with individuals colored by cluster (2D).
- The original data with a supplementary row called class containing the partition.
- The description of the classes by the factors (axes). See
- The description of the classes by the variables. See
- A list or parameters and internal objects.
- The paragons (para) and the more typical individuals of each cluster. See details.
## Not run: data(iris) # Principal Component Analysis: res.pca <- PCA(iris[,1:4], graph=FALSE) # Clustering, auto nb of clusters: hc <- HCPC(res.pca, nb.clust=-1) ### Construct a hierarchical tree from a partition (with 10 clusters) ### (useful when the number of individuals is very important) hc2 <- HCPC(iris[,1:4], kk=10, nb.clust=-1) ## End(Not run)
Documentation reproduced from package FactoMineR, version 1.25. License: GPL (>= 2)