| treeClust {treeClust} | R Documentation |
Build a tree-based dissimilarity for clustering, and optionally perform the clustering
Description
This function uses a set of classification or regression trees to build an inter-point dissimilarity in which two points are similar when they tend to fall in the same leaves of trees. The user can pass in a clustering algorithm and/or ask for the dissimilarities or the set of trees.
Usage
treeClust(dfx, d.num = 1, col.range = 1:ncol(dfx), verbose = F,
final.algorithm, k, control = treeClust.control(), rcontrol = rpart.control(), ...)
Arguments
dfx |
Input data frame. Columns may be numeric or categorical. Missing values are permitted. |
d.num |
Integer: Dissimilarity specifier. When d.num = 1, the dissimilarity between two observations is the proportion of trees where they disagree. With d.num = 2, those counts are weighted according to tree quality. In d.num = 3, dissimilarities are variable with trees, reflecting the belief that some pairs of leaves are closer together than others. With d.num = 4, those dissimilarities are weighted by tree quality. |
col.range |
Integer: the indices of the columns used. Defaults to all. |
verbose |
If non-zero, print degugging messages to the screen. |
final.algorithm |
Final algorithm, to be used to cluster the computed distances. This may be "pam", "agnes", "clara" or "kmeans". |
k |
If final.algorithm is supplied, the number of clusters is required. |
control |
List of the sort produced by |
rcontrol |
List of the sort produced by |
... |
Other arguments, to be passed to the final clustering algorithm if specified. |
Details
The treeClust approach builds a set of classification or regresion trees, one for each variable. Trees are pruned, and those that are pruned to the root are discarded. For each remaining tree, an observation's leaf membership serves as the starting point for a dissimilarity measurement.
Value
If control$cluster.only is TRUE, a vector of cluster assignments, as produced by the final algorthm. Otherwise, a list with these items:
call |
The call that produced the object |
d.num |
d.num, as supplied |
tbl |
Two-column matrix with one row for each tree retained, giving size and deviance ratio |
extended.tbl |
Two-column matrix like tbl, but with one row for every variable, giving size and deviance ratio (these will be 1 and 0 for variables whose trees were discarded |
final.algorithm |
final.algorithm, as supplied |
final.clust |
If final.algorithm is supplied, the output from the final clustering algorithm; otherwise, NULL |
additional.args |
Any additional arguments specified |
tree |
If control$return.trees is TRUE, a list holding all the retained trees. This can make the resulting object very large. |
dists |
If control$return.dists is TRUE, an object of class dist with the set of pairwise inter-point dissimilarities |
mat |
If control$return.mat is TRUE, a data frame. If final.algorithm is "pam" or "agnes" this contains leaf assignment indices. Otherwise this holds a dataset useful as input to k-means or clara. Experimental. |
Author(s)
Sam Buttrey, buttrey@nps.edu
References
Buttrey and Whitaker, "treeClust: An R Package for Tree-Based Clustering Dissimilarities," The R Journal, 7/2, 2015.
See Also
Examples
iris.km6 <- treeClust (iris[,-5], d.num = 2, final.algorithm = "kmeans", k=6)
table (iris.km6$final.clust$cluster, iris$Species)