FindDistm

boolean. Normalize data before calculating distance?

normalize

method


A wrapper of the dist() method, with the option to rescale the data with standard deviation of each dimension before calculating the distance matrix. NOTE: If fdelta='mnorm' is passed to adpclust(), then the distm is calculated from rescaled data internally, i.e. distm &lt;- FindDistm(x, normalize = TRUE).


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.

Yifan Ethan Xu

ADPclust

Fast Clustering Using Adaptive Density Peak Detection

FindDistm function


A wrapper of the dist() method, with the option to rescale the data with standard deviation of each dimension before calculating the distance matrix. NOTE: If fdelta='mnorm' is passed to adpclust(), then the distm is calculated from rescaled data internally, i.e. distm <- FindDistm(x, normalize = TRUE).


FindDistm: Find the distance matrix from data.

Description

Usage

Arguments

Value