| Name | global-kmeans-pp JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | |
| Summary | A library for implementing the global k-means and the global k-means++ clustering algorithms. |
| upload_time | 2023-07-25 10:18:26 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.10 |
| license | MIT License Copyright (c) 2022 Giorgos Vardakas Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
clustering
k-means
clustering error
global optimization
global k-means
k-means++
global k-means++
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# The Global $k$-mean++ clustering algorithm
The global $k$-means++ is an effective relaxation of the global $k$-means clustering algorithm, providing an ideal compromise between clustering error and execution speed. It is an effective way of acquiring quality clustering solutions akin to those of global $k$-means with a reduced computational load. It is an incremental clustering approach that dynamically adds one cluster center at each $k$ cluster sub-problem. For each $k$ cluster sub-problem, the method selects $L$ data points as candidates for the initial position of the new center using the effective $k$-means++ selection probability distribution. The selection method is fast and requires no extra computational effort for distance computations.
```
@article{vardakas2022global,
title={Global $k$-means$++$: an effective relaxation of the global $k$-means clustering algorithm},
author={Vardakas, Georgios and Likas, Aristidis},
journal={arXiv preprint arXiv:2211.12271},
year={2022}
}
@article{likas2003global,
title={The global k-means clustering algorithm},
author={Likas, Aristidis and Vlassis, Nikos and Verbeek, Jakob J},
journal={Pattern recognition},
volume={36},
number={2},
pages={451--461},
year={2003},
publisher={Elsevier}
}
```
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