estimateDc: Estimate the distance cutoff for a specified neighbor rate

In densityClust: Clustering by Fast Search and Find of Density Peaks

Estimate the distance cutoff for a specified neighbor rate

Description

This function calculates a distance cutoff value for a specific distance matrix that makes the average neighbor rate (number of points within the distance cutoff value) fall between the provided range. The authors of the algorithm suggests aiming for a neighbor rate between 1 and 2 percent, but also states that the algorithm is quite robust with regards to more extreme cases.

Usage

estimateDc(distance, neighborRateLow = 0.01, neighborRateHigh = 0.02)

Arguments

distance	A distance matrix
neighborRateLow	The lower bound of the neighbor rate
neighborRateHigh	The upper bound of the neighbor rate

Value

A numeric value giving the estimated distance cutoff value

Note

If the number of points is larger than 448 (resulting in 100,128 pairwise distances), 100,128 distance pairs will be randomly selected to speed up computation time. Use set.seed() prior to calling estimateDc in order to ensure reproducable results.

References

Rodriguez, A., & Laio, A. (2014). Clustering by fast search and find of density peaks. Science, 344(6191), 1492-1496. doi:10.1126/science.1242072

Examples

irisDist <- dist(iris[,1:4])
estimateDc(irisDist)

densityClust documentation built on March 18, 2022, 7:42 p.m.