k-means-constrained
K-Means clustering constrained with minimum and maximum cluster size
K-Means clustering constrained with minimum and maximum cluster size
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K-means clustering implementation whereby a minimum and/or maximum size for each cluster can be specified. This K-means implementation modifies the cluster assignment step (E in EM) by formulating it as a Minimum Cost Flow (MCF) linear network optimisation problem. This is then solved using a cost-scaling push-relabel algorithm and uses Google's Operations Research tools's SimpleMinCostFlow which is a fast C++ implementation.
Summary
K-Means clustering constrained with minimum and maximum cluster size
Last Updated
May 19, 2023 at 17:19
License
BSD-3-Clause
Total Downloads
14.0K
Supported Platforms
GitHub Repository
https://github.com/joshlk/k-means-constrainedDocumentation
https://joshlk.github.io/k-means-constrained/