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k-means-constrained

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K-Means clustering constrained with minimum and maximum cluster size

Installation

To install this package, run one of the following:

Conda
$conda install sfe1ed40::k-means-constrained

Usage Tracking

0.7.2
1 / 8 versions selected
Downloads (Last 6 months): 0

Description

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.

About

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

linux-ppc64le
macOS-arm64
linux-64
linux-aarch64
linux-s390x
macOS-64