Title: Fast Clustering Using Adaptive Density Peak Detection
Description: An implementation of ADPclust clustering procedures (Fast Clustering Using Adaptive Density Peak Detection). The work is built and improved upon Rodriguez and Laio[2014]'s idea. 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 an user interactive algorithm that allows the user to manually selects cluster centroids from a two dimensional "density-distance plot".
Author: Yifan "Ethan" Xu <ethan.yifanxu@gmail.edu>, Xiao-Feng Wang <wangx6@ccf.org>
Maintainer: Yifan "Ethan" Xu <ethan.yifanxu@gmail.com>

Authors@R: c(person("Yifan \"Ethan\"", "Xu", email = "ethan.yifanxu@gmail.com", role = c("aut","cre")), person("Xiao-Feng", "Wang", email = "wangx6@ccf.org", role = "aut"))
Description: An implementation of ADPclust clustering procedures (Fast
Clustering Using Adaptive Density Peak Detection). The work is built and
improved upon Rodriguez and Laio[2014]'s idea. 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 an user interactive algorithm that allows the user
to manually selects cluster centroids from a two dimensional "density-distance

Authors@R: c(person("Yifan (Ethan) Xu", email = "ethan.yifanxu@gmail.com", role = c("aut","cre")), person("Xiao-Feng", "Wang", email = "wangx6@ccf.org", role = "aut"))
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:
Maintainer: Yifan (Ethan) Xu <ethan.yifanxu@gmail.com>