estimateDc

This function calculates a distance cutoff value for a specific distance
matrix that makes the average neighbor rate (number of points within the
distance cutoff value) fall between the provided range. The authors of the
algorithm suggests aiming for a neighbor rate between 1 and 2 percent, but
also states that the algorithm is quite robust with regards to more extreme
cases.

An improved implementation (based on k-nearest neighbors) of
the density peak clustering algorithm, originally described by Alex
Rodriguez and Alessandro Laio (Science, 2014 vol. 344). It can handle large
datasets (> 100,000 samples) very efficiently. It was initially implemented
by Thomas Lin Pedersen, with inputs from Sean Hughes and later improved by
Xiaojie Qiu to handle large datasets with kNNs.

Thomas Pedersen

densityClust

Clustering by Fast Search and Find of Density Peaks

Thomas Lin Pedersen

Sean Hughes

Xiaojie Qiu

estimateDc function

<dl><dt>distance</dt>
<dd>A distance matrix</dd>
<dt>neighborRateLow</dt>
<dd>The lower bound of the neighbor rate</dd>
<dt>neighborRateHigh</dt>
<dd>The upper bound of the neighbor rate</dd></dl>

Arguments

Estimate the distance cutoff for a specified neighbor rate — estimateDc

<dl>

<dt>distance</dt>
<dd>A distance matrix</dd>


<dt>neighborRateLow</dt>
<dd>The lower bound of the neighbor rate</dd>


<dt>neighborRateHigh</dt>
<dd>The upper bound of the neighbor rate</dd>

</dl>

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estimateDc: Estimate the distance cutoff for a specified neighbor rate

Description

Usage

Value

Arguments

References

Examples