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In Affinity Product clustering, IC measures, except equiv. dipoles, (ERP, ERSP...) are compared for each IC pair and their dissimilarity is multiplied together to form a combined pairwise dissimilarity matrix. This matrix is then normalized, weighted and added to the normalized and weighted IC equiv. dipole distance matrix. The final dissimilarity matrix is then clustered using affinity clustering method (Fig. below).
As you can see this method does not perform any dimensionality reduction on EEG measures (i.e PCA) as it only calculates pairwise (dis)similarities.These similarity matrices (correlations) has to be calculated in the pre-clustering step.
Invoke the pre-clustering graphic interface by using menu item Study → Affinity Product clustering → Build pre-clustering array.
In the GUI you can select EEG measures for which pre-clustering matrices should be calculated.
Use 'Re-Calculate All' option to remove all these matrices before calculating new ones. This might be useful if you have changed the subset of STUDY components to be clustered.
After the pre-clustering step (above) is finished, you can cluster STUDY components based on any combination of measures included in pre-clustering. The final clustering is performed on the combined pairwise distance matrix using Affinity Propagation algorithm.
Invoke the MP clustering graphic interface by using menu item Study → Affinity Product clustering → Cluster Components will open the following window.
Here you can specify the number of clusters and control the effect of equiv. dipole distances in the clustering by setting the 'Relative dipole weight' parameter. For example, by setting this value to 0.8, the final dissimilarity matrix will consist of 80% distance dissimilarity and 20% of other measures combined together.
Please note that the number of returned clusters may slightly (up to 5%) differ from the number requested in the GUI. Also, currently only clustering the parent cluster (containing all components) is supported.
An option for the Affinity Clustering algorithm can relegate 'outlier' components to a separate cluster. Outlier components are defined as components further than a specified number of standard deviations (3, by default) from any of the cluster centroids. To turn on this option, click the lower checkbox on the left. Identified outlier components will be put into a designated Outliers cluster (Cluster 2).