WH_hclust {HistDAWass} | R Documentation |
Hierarchical clustering of histogram data
Description
The function implements a Hierarchical clustering
for a set of histogram-valued data, based on the L2 Wassertein distance.
Extends the hclust
function of the stat package.
Usage
WH_hclust(
x,
simplify = FALSE,
qua = 10,
standardize = FALSE,
distance = "WDIST",
method = "complete"
)
Arguments
x |
A MatH object (a matrix of distributionH). |
simplify |
A logic value (default is FALSE), if TRUE histograms are recomputed in order to speed-up the algorithm. |
qua |
An integer, if |
standardize |
A logic value (default is FALSE). If TRUE, histogram-valued data are standardized, variable by variable, using the Wassertein based standard deviation. Use if one wants to have variables with std equal to one. |
distance |
A string default "WDIST" the L2 Wasserstein distance (other distances will be implemented) |
method |
A string, default="complete", is the the agglomeration method to be used.
This should be (an unambiguous abbreviation of) one of " |
Value
An object of class hclust which describes the tree produced by the clustering process.
References
Irpino A., Verde R. (2006). A new Wasserstein based distance for the hierarchical clustering of histogram symbolic data. In: Batanjeli et al. Data Science and Classification, IFCS 2006. p. 185-192, BERLIN:Springer, ISBN: 3-540-34415-2
See Also
hclust
of stat package for further details.
Examples
results <- WH_hclust(x = BLOOD, simplify = TRUE, method = "complete")
plot(results) # it plots the dendrogram
cutree(results, k = 5) # it returns the labels for 5 clusters