NNS.SD.cluster: NNS SD-based Clustering

In NNS: Nonlinear Nonparametric Statistics

NNS SD-based Clustering

Description

Clusters a set of variables by iteratively extracting Stochastic Dominance (SD)-efficient sets, subject to a minimum cluster size.

Usage

NNS.SD.cluster(
  data,
  degree = 1,
  type = "discrete",
  min_cluster = 1,
  dendrogram = FALSE
)

Arguments

data	A numeric matrix or data frame of variables to be clustered.
degree	Numeric options: (1, 2, 3). Degree of stochastic dominance test.
type	Character, either "discrete" (default) or "continuous"; specifies the type of CDF.
min_cluster	Integer. The minimum number of elements required for a valid cluster.
dendrogram	Logical; FALSE (default). If TRUE, a dendrogram is produced based on a simple "distance" measure between clusters.

Details

The function applies NNS.SD.efficient.set iteratively, peeling off the SD-efficient set at each step if it meets or exceeds min_cluster in size, until no more subsets can be extracted or all variables are exhausted. Variables in each SD-efficient set form a cluster, with any remaining variables aggregated into the final cluster if it meets the min_cluster threshold.

Value

A list with the following components:

• Clusters: A named list of cluster memberships where each element is the set of variable names belonging to that cluster.

• Dendrogram (optional): If dendrogram = TRUE, an hclust object is also returned.

Author(s)

Fred Viole, OVVO Financial Systems

References

Viole, F. and Nawrocki, D. (2016) "LPM Density Functions for the Computation of the SD Efficient Set." Journal of Mathematical Finance, 6, 105-126. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.4236/jmf.2016.61012")}.

Viole, F. (2017) "A Note on Stochastic Dominance." \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2139/ssrn.3002675")}

Examples

## Not run: 
set.seed(123)
x <- rnorm(100)
y <- rnorm(100)
z <- rnorm(100)
A <- cbind(x, y, z)

# Perform SD-based clustering (degree 1), requiring at least 2 elements per cluster
results <- NNS.SD.cluster(data = A, degree = 1, min_cluster = 2)
print(results$Clusters)

# Produce a dendrogram as well
results_with_dendro <- NNS.SD.cluster(data = A, degree = 1, min_cluster = 2, dendrogram = TRUE)

## End(Not run)

NNS documentation built on April 3, 2025, 8:39 p.m.