cluster_new

Given training data, test data, and kkmeans-results,
get which partitions a new set of data belong to

Implementations several algorithms for kernel k-means. The default
'OTQT' algorithm is a fast alternative to standard implementations of kernel
k-means, particularly in cases with many clusters. For a small number of
clusters, the implemented 'MacQueen' method typically performs the fastest. For
more details and performance evaluations, see Berlinski and Maitra (2025) <doi:10.1002/sam.70032>.

Josh Berlinski

kkmeans

Fast Implementations of Kernel K-Means

cluster_new function

<dl><dt>test_data</dt>
<dd>the new data to be classified</dd>
<dt>train_data</dt>
<dd>the data to make classifications from</dd>
<dt>train_result</dt>
<dd><code>kkmeans</code> result containing the result from classifying
<code>train_data</code></dd></dl>

Arguments

Classify new data based on kkmeans result — cluster_new

<dl>

<dt>test_data</dt>
<dd>the new data to be classified</dd>


<dt>train_data</dt>
<dd>the data to make classifications from</dd>


<dt>train_result</dt>
<dd><code>kkmeans</code> result containing the result from classifying
<code>train_data</code></dd>

</dl>

cluster_new: Classify new data based on kkmeans result

Description

Usage

Value

Arguments