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.
|Author||Yifan (Ethan) Xu [aut, cre], Xiao-Feng Wang [aut]|
|Date of publication||2016-10-15 11:37:01|
|Maintainer||Yifan (Ethan) Xu <firstname.lastname@example.org>|
|License||GPL (>= 2)|
adpclust: Fast Clustering Using Adaptive Density Peak Detection
AMISE: AMISE bandwidth
clust10: 1000 5-dimensional data points that form ten clusters
clust3: 90 2-dimensional data points that form three clusters
clust5: 500 5-dimensional data points that form five clusters
clust5.1: 500 5-dimensional data points that form five clusters
dat_gene: 243-dimensional gene expression data of 38 patients (243...
defCol: Default colors
FindCentersAutoD: Automatically finds centers with diagonal f(x) vs delta(x)...
FindCentersAutoV: Automatically find centers with vertical threshold
FindClustersAuto: Automatically find cluster assignment given f and delta.
FindClustersGivenCenters: Find cluster assignments given centers and distance matrix
FindClustersManual: User-interactive routine to find clusters
FindDistm: Find the distance matrix from data.
FindFD: Find f and delta from distance matrix.
FindH: Find bandwidth h.
plot.adpclust: Visualize the result of adpclust()
ROT: Calculate ROT bandwidth
summary.adpclust: Summary of adpclust