medinfo: compute info on distances to medoids/centroids

In ksharp: Cluster Sharpening

Description

Analogous in structure to silinfo and neiinfo, it computes a "widths" matrix assessing how well each data point belongs to its cluster. Here, this measure is the ratio of two distances: in the numerator, the distance from the point to the nearest cluster center, and in the denominator, from the point to its own cluster center.

Usage

medinfo(cluster, data, silwidths)

Arguments

cluster	named vector
data	matrix with raw data
silwidths	matrix with silhouette widths

Value

list with component widths. The widths object is a matrix with one row per data item, with column med_ratio holding the sharpness measure.

Examples

# construct a manual clustering of the iris dataset
iris.data = iris[, 1:4]
rownames(iris.data) = paste0("iris_", seq_len(nrow(iris.data)))
iris.dist = dist(iris.data)
iris.clusters = setNames(as.integer(iris$Species), rownames(iris.data))

# compute sharpnessvalues based on medoids
iris.silinfo = silinfo(iris.clusters, iris.dist)
medinfo(iris.clusters, iris.data, iris.silinfo$widths)

ksharp documentation built on Jan. 26, 2020, 5:06 p.m.