Fast Clustering Using Adaptive Density Peak Detection
Fast Clustering Using Adaptive Density Peak Detection
AMISE bandwidth
Default colors
Automatically finds centers with diagonal f(x) vs delta(x) thresholds
Automatically find centers with vertical threshold
Automatically find cluster assignment given f and delta.
Find cluster assignments given centers and distance matrix
User-interactive routine to find clusters
Find the distance matrix from data.
Find f and delta from distance matrix.
Find bandwidth h.
Visualize the result of adpclust()
Calculate ROT bandwidth
Summary of adpclust
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.