scikit-learn-contrib/denmune-clustering-algorithm
DenMune a clustering algorithm that can find clusters of arbitrary size, shapes and densities in two-dimensions. Higher dimensions are first reduced to 2-D using the t-sne. The algorithm relies on a single parameter K (the number of nearest neighbors). The results show the superiority of DenMune. Enjoy the simplicty but the power of DenMune.
This tool helps scientists, researchers, and data analysts discover natural groupings within complex datasets. You provide your data, which can be high-dimensional, and it automatically identifies clusters of varying sizes, shapes, and densities. The output is a clear categorization of your data points into distinct groups.
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Use this if you need to find hidden patterns and natural clusters in your data without having to specify the number of clusters or their expected shapes beforehand.
Not ideal if you already know the number of clusters you expect or if your data clusters are uniformly sized and shaped.
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BSD-3-Clause
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Last pushed
Mar 07, 2025
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