Encoding:	UTF-8
Title:	Calculates the Density-Based Clustering Validation (DBCV) Index
Version:	1.4
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>.
Depends:	R (≥ 4.0.0)
License:	GPL-3
URL:	https://github.com/davidechicco/DBCVindex
BugReports:	https://github.com/davidechicco/DBCVindex/issues
Imports:	qpdf
Suggests:	knitr, rmarkdown
VignetteBuilder:	knitr
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-02-19 09:02:09 UTC; davide
Author:	Davide Chicco [cre], Pablo Andretta Jaskowiak [aut]
Maintainer:	Davide Chicco <davidechicco@davidechicco.it>
Repository:	CRAN
Date/Publication:	2025-02-19 23:40:15 UTC

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

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 Density-Based Clustering Validation index (DBCV) of clustering results

Description

Function that calculates the Density-Based Clustering Validation index (DBCV) of clustering results

Usage

dbcv_index(data, partition, noiseLabel = -1)

Arguments

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(X, y) = ", dbcv_index(X, y), "\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

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)