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<h2>Clustering</h2>
The Clustering panel allows to test, tweak and observe how different
algorithms perform clustering on samples living in a N-dimensional
space: "the canvas".<br>
Clustering can assign a cluster value to each sample, or a
contribution of each cluster, or a non-cluster value (for outliers),
depending on the algorithm used.<br>
<br>
The canvas will display the results of the clustering in multiple
layers, which can be toggled using the display options. These are:<br>
<ul>
<li>Samples: the original sample data, if the samples have class
information, these will be displayed using different colors </li>
<li>Learned Model: the cluster labels obtained by the algorithm </li>
<li>Model Info: additional information from the algorithm
(gaussian position and shape, support vectors, etc.)</li>
<li>Density Map: (for 2D canvas only) clustering result for each
coordinate in space</li>
</ul>
In the standard case, a different color is assigned to each cluster.
For algorithms providing a contribution from the cluster, a mixing
of colors means multiple contributions from different clusters. Be
advised that these colors do not correspond to class labels (indeed
the data could have no class information whatsoever) but rather they
indicate the cluster(s) they have been assigned to.<br>
<br>
<span style="font-weight: bold;">In Practice</span><br>
The easiest way to perform clustering is to:
<ol>
<li>Draw some samples (left-click)</li>
<li>Click on "Cluster" </li>
</ol>
This should train the algorithm and start painting the canvas with
the results of the clustering<br>
<br>
<span style="font-weight: bold;">Options and Commands</span><br>
The interface for clustering (the right-hand side of the Algorithm
Options dialog) provides the following commands:
<ul>
<li>Cluster: perform the clustering using the currently selected
algorithm and options</li>
<li>One Iteration: (for iterative algorithms only) perform a
single iteration of the clustering algorithm</li>
<li>Clear: clear the current classifier model (does NOT clear the
data)</li>
<li>Manual Selection: manually select the samples used to train
the clusterer </li>
<li>Optimize Clusters: perform multiple clustering with different
counts of starting clusters (when applies), compute the
clustering quality for each cluster count and pick the optimum
within the set range</li>
<li>Test: compute the clustering quality for a single instance of
the clusterer, using the current options </li>
</ul>
and the following options:
<ul>
<li>Optimize by: determine the clustering quality function to be
used for the Optimize Clusters command, currently include:</li>
<ul>
<li>RSS (Residual Sum of Squares): the squared distance between
each sample and the center of its associated cluster</li>
<li>BIC: Bayesian Information Criterion</li>
<li>AIC: Akaike Information Criterion</li>
<li>F1: F-Measure score (requires class-labeled data), compute
the F-Measure for each class and each cluster (picks the
maximum cluster for each class) </li>
</ul>
<li>Train Ratio: amount of data used to compute the curent
clustering quality using the Test button</li>
</ul>
All other options are algorithm-dependent and should be described in
the help menu of the algorithm itself.<br>
<br>
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