Metadata-Version: 2.4
Name: connected-k-center
Version: 0.1
Summary: Implementations of algorithms for connected k-center on path graphs.
License: MIT
License-File: LICENSE
Author: Lukas Drexler
Author-email: lukas.drexler@hhu.de
Requires-Python: >=3.10,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Dist: numpy (>=1.26.4,<2.0.0)
Requires-Dist: scikit-learn (>=1.6.1,<2.0.0)
Description-Content-Type: text/markdown

# Connected Path Graph Clustering

A library for algorithms for the connected k-center problem (as described in [1]). In this problem setting, the input consists of a point set $P$ and a desired number of centers $k$, along with a *connectivity graph* $G = (P,E)$. The goal is to partition $P$ into (at most) $k$ *clusters* $C_1, \ldots, C_k$, such that, for every $i$, the subgraph of $G$ induced by $C_i$ is connected.

As of now, only an algorithm for path graphs is implemented. A path graph is a graph whose connected components are simple paths. This algorithm was developed by Johanna Hillebrand and implemented by Julius Mann.


### References
[1] Drexler, L., Eube, J., Luo, K., Reineccius, D., Röglin, H., Schmidt, M., & Wargalla, J. (2024). Connected k-center and k-diameter clustering. Algorithmica, 86(11), 3425-3464.

