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2021

Flattening Multiparameter Hierarchical Clustering Functors

Shiebler, Dan

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We bring together topological data analysis, applied category theory, and machine learning to study multiparameter hierarchical clustering.

We begin by introducing a procedure for flattening multiparameter hierarchical clusterings.
We demonstrate that this procedure is a functor from a category of multiparameter hierarchical partitions to a category of binary integer programs.
We also include empirical results demonstrating its effectiveness.

Flattening Multiparameter Hierarchical Clustering Functors · Around

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Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)
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Shiebler, D.: Functorial clustering via simplicial complexes. NeurIPS Workshop on Topological Data Analysis in ML (2020)
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