FindFD

distm

character string that specifies the method used to estimate local density f(x) at each data point x. The default is "mnorm" that uses a multivariate Gaussian density estimation to calculate f. Other options are listed below. Here 'distm' denotes the distance matrix. 
<ul>
<li>unorm(f &lt;- 1/(h * sqrt(2 * pi)) * rowSums(exp(-(distm/h)^2/2))); Univariate Gaussian smoother
</li>
<li>weighted(f &lt;- rowSums(exp(-(distm/h)^2))); Univariate weighted smoother
</li>
<li>count(f &lt;- rowSums(distm < h) - 1); Histogram estimator (used in Rodriguez [2014])
</li>
</ul>


fdelta


Calculate f(x) and delta(x) from distm and h.


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

FindFD function

character string that specifies the method used to estimate local density f(x) at each data point x. The default is "mnorm" that uses a multivariate Gaussian density estimation to calculate f. Other options are listed below. Here 'distm' denotes the distance matrix. 
<ul>
<li>unorm(f <- 1/(h * sqrt(2 * pi)) * rowSums(exp(-(distm/h)^2/2))); Univariate Gaussian smoother
</li>
<li>weighted(f <- rowSums(exp(-(distm/h)^2))); Univariate weighted smoother
</li>
<li>count(f <- rowSums(distm < h) - 1); Histogram estimator (used in Rodriguez [2014])
</li>
</ul>


FindFD: Find f and delta from distance matrix.

Description

Usage

Arguments

Value