treeClust: Build a tree-based dissimilarity for clustering, and...

In treeClust: Cluster Distances Through Trees

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 treeClust.control, giving specifications for the fitting routine.
rcontrol	List of the sort produced by rpart.control, giving arguments for the rpart routine.
...	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, [e-mail]

References

Buttrey and Whitaker, "treeClust: An R Package for Tree-Based Clustering Dissimilarities," The R Journal, 7/2, 2015.

See Also

treeClust.control

Examples

iris.km6 <- treeClust (iris[,-5], d.num = 2, final.algorithm = "kmeans", k=6)
table (iris.km6$final.clust$cluster, iris$Species)

Example output

Loading required package: rpart
Loading required package: cluster
   
    setosa versicolor virginica
  1      0          1        12
  2     50          0         0
  3      0          5         9
  4      0          0        10
  5      0         44         0
  6      0          0        19

treeClust documentation built on May 1, 2019, 7:59 p.m.