FindClustersAuto

distm

vector of local distance f(x). See the help of adpclust() for details.

vector of minimal distances to higher ground delta(x). See the help of adpclust() for details.

delta

type of auto selection. The valid options are 1 and 2. See the help of adpclust() for details.

number of clusters to test. Either a single integer or a vector of integers.

nclust

number between (0, 1) or numeric vector of numbers between (0, 1). Data points whose f values are larger than f.cut with large delta values are selected as centers. The default is c(0.1, 0.2, 0.3). See the help of FindCentersAutoV() for more details.

f.cut


This is the subroutine that automatically finds cluster assignments from given f and delta by testing various parameter settings and find the one that maximizes the silhouette.


An implementation of ADPclust clustering procedures (Fast
Clustering Using Adaptive Density Peak Detection). The work is built and
improved upon the idea of Rodriguez and Laio (2014)<DOI:10.1126/science.1242072>.
ADPclust clusters data by finding density peaks in a density-distance plot
generated from local multivariate Gaussian density estimation. It includes
an automatic centroids selection and parameter optimization algorithm, which
finds the number of clusters and cluster centroids by comparing average
silhouettes on a grid of testing clustering results; It also includes a user
interactive algorithm that allows the user to manually selects cluster
centroids from a two dimensional "density-distance plot". Here is the
research article associated with this package: "Wang, Xiao-Feng, and
Yifan Xu (2015)<DOI:10.1177/0962280215609948> Fast clustering using adaptive
density peak detection." Statistical methods in medical research". url:
http://smm.sagepub.com/content/early/2015/10/15/0962280215609948.abstract.

FindClustersAuto: Automatically find cluster assignment given f and delta.

Description

Usage

Arguments

Value