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Gaussian Mixture & K-Means Clustering


LunchBoxML implements Gaussian Mixture and K-Means Clustering machine learning algorithms using the Accord.NET framework.

These algorithms are useful in sorting objects together that share similar attributes into a set number of groups. While Gaussian Mixture and K-Means can serve similar purposes, the specifics of the clustering problem may drive the user to select one algorithm over the other.

Gaussian Panel Groupings

This Grasshopper example employ the Gaussian Mixture components to identify groupings of 3D panel geometry that share similar dimensional characteristics with the aim of supporting further design and fabrication activities.

The example can be extended with additional numerical attributes to contribute to the groups with controls over attribute weights and group quantity.

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