Package 'DBCVindex'

Title: Calculates the Density-Based Clustering Validation (DBCV) Index
Description: A metric called 'Density-Based Clustering Validation index' (DBCV) index to evaluate clustering results, following the <https://github.com/pajaskowiak/clusterConfusion/blob/main/R/dbcv.R> 'R' implementation by Pablo Andretta Jaskowiak. Original 'DBCV' index article: Moulavi, D., Jaskowiak, P. A., Campello, R. J., Zimek, A., and Sander, J. (April 2014), "Density-based clustering validation", Proceedings of SDM 2014 -- the 2014 SIAM International Conference on Data Mining (pp. 839-847), <doi:10.1137/1.9781611973440.96>. A more recent article on the 'DBCV' index: Chicco, D., Sabino, G.; Oneto, L.; Jurman, G. (August 2025), "The DBCV index is more informative than DCSI, CDbw, and VIASCKDE indices for unsupervised clustering internal assessment of concave-shaped and density-based clusters", PeerJ Computer Science 11:e3095 (pp. 1-), <doi:10.7717/peerj-cs.3095>.
Authors: Davide Chicco [cre] (ORCID: <https://orcid.org/0000-0001-9655-7142>), Pablo Andretta Jaskowiak [aut] (ORCID: <https://orcid.org/0000-0002-6377-3372>)
Maintainer: Davide Chicco <[email protected]>
License: GPL-3
Version: 1.6
Built: 2026-05-12 08:14:05 UTC
Source: https://github.com/cran/DBCVindex

Help Index

Function that calculates the original Density-Based Clustering Validation index (DBCV) of clustering results between -1 and +1

Description

Function that calculates the original Density-Based Clustering Validation index (DBCV) of clustering results between -1 and +1

Usage

dbcv_index(data, partition, noiseLabel = -1)

Arguments

data

input clustering results
partition

labels of the clustering
noiseLabel

the code of the noise cluster points, -1 by default

Value

a real value containing the DBCV coefficient in the [-1;+1] interval

Examples

n = 300; noise = 0.05;
 seed = 1782;
 theta <- seq(0, pi, length.out = n / 2)
 x1 <- cos(theta) + rnorm(n / 2, sd = noise)
 y1 <- sin(theta) + rnorm(n / 2, sd = noise)
 x2 <- cos(theta + pi) + rnorm(n / 2, sd = noise)
 y2 <- sin(theta + pi) + rnorm(n / 2, sd = noise)
 X <- rbind(cbind(x1, y1), cbind(x2, y2))
 y <- c(rep(0, n / 2), rep(1, n / 2))

cat("DBCV index of (X, y) = ", dbcv_index(X, y), " in the [-1;+1] interval\n", sep="")

Function that calculates the mutual reachability distance within a matrix

Description

Function that calculates the mutual reachability distance within a matrix

Usage

matrix_mutual_reachability_distance(MinPts, G_edges_weights, d)

Arguments

MinPts

number of minimal points
G_edges_weights

matrix of edges weights
d

number of features

Value

a list of two elements: d_ucore and G_edges_weights

Examples

n = 300; noise = 0.05; seed = 1782;
 theta <- seq(0, pi, length.out = n / 2)
 x1 <- cos(theta) + rnorm(n / 2, sd = noise)
 y1 <- sin(theta) + rnorm(n / 2, sd = noise)
 x2 <- cos(theta + pi) + rnorm(n / 2, sd = noise)
 y2 <- sin(theta + pi) + rnorm(n / 2, sd = noise)
 X <- rbind(cbind(x1, y1), cbind(x2, y2))
 y <- c(rep(0, n / 2), rep(1, n / 2))

nfeatures <- ncol(X)
i <- 1
clusters <- unique(y)
objcl <- which(y == clusters[i])
nuobjcl <- length(objcl)

noiseLabel <- -1
distX <- as.matrix(dist(X))^2
distXy <- distX[y != noiseLabel, y != noiseLabel]

mr <- matrix_mutual_reachability_distance(nuobjcl, distXy[objcl, objcl], nfeatures)

Function that finds the list of MST edges

Description

Function that finds the list of MST edges

Usage

MST_Edges(G, start, G_edges_weights)

Arguments

G

list of four elements: number of vertices, MST_edges (matrix of edges), MST_degrees (array of numbers), MST_parent (array of numbers)
start

index of the first edge
G_edges_weights

matrix of edges weights

Value

list of two elements: matrix of edges and array of degrees

Examples

n = 300; noise = 0.05;
seed = 1782;
theta <- seq(0, pi, length.out = n / 2)
x1 <- cos(theta) + rnorm(n / 2, sd = noise)
y1 <- sin(theta) + rnorm(n / 2, sd = noise)
x2 <- cos(theta + pi) + rnorm(n / 2, sd = noise)
y2 <- sin(theta + pi) + rnorm(n / 2, sd = noise)
X <- rbind(cbind(x1, y1), cbind(x2, y2))
 y <- c(rep(0, n / 2), rep(1, n / 2))

nfeatures <- ncol(X)
i <- 1
clusters <- unique(y)
objcl <- which(y == clusters[i])
nuobjcl <- length(objcl)

noiseLabel <- -1
distX <- as.matrix(dist(X))^2
distXy <- distX[y != noiseLabel, y != noiseLabel]

mr <- matrix_mutual_reachability_distance(nuobjcl, distXy[objcl, objcl], nfeatures)

d_ucore_cl <- rep(0, nrow(X))
d_ucore_cl[objcl] <- mr$d_ucore
G <- list(no_vertices = nuobjcl, MST_edges = matrix(0, nrow = nuobjcl - 1, ncol = 3),
         MST_degrees = rep(0, nuobjcl), MST_parent = rep(0, nuobjcl))
g_start <- 1

mst_results <- MST_Edges(G, g_start, mr$G_edges_weights)

Function that calculates the normalized Density-Based Clustering Validation index (DBCV) of clustering results between 0 and 1

Description

Function that calculates the normalized Density-Based Clustering Validation index (DBCV) of clustering results between 0 and 1

Usage

norm_dbcv_index(data, partition, noiseLabel = -1)

Arguments

data

input clustering results
partition

labels of the clustering
noiseLabel

the code of the noise cluster points, -1 by default

Value

a real value containing the normalized DBCV coefficient in the [0;1] interval

Examples

n = 200; noise = 0.04;
 seed = 1783;
 theta <- seq(0, pi, length.out = n / 2)
 x1 <- cos(theta) + rnorm(n / 2, sd = noise)
 y1 <- sin(theta) + rnorm(n / 2, sd = noise)
 x2 <- cos(theta + pi) + rnorm(n / 2, sd = noise)
 y2 <- sin(theta + pi) + rnorm(n / 2, sd = noise)
 X <- rbind(cbind(x1, y1), cbind(x2, y2))
 y <- c(rep(0, n / 2), rep(1, n / 2))

cat("normalized DBCV index of (X, y) = ", norm_dbcv_index(X, y), " in the [0;1] interval\n", sep="")