| 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
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 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
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(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
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)