KMOR Numpy
The python implementation for k-means clustering with outlier removal from the paper written by Guojun Gan et al. [1]
Installation
pip install kmor
You can also install by conda
conda install -c ksunhokim kmor
Example
import numpy as np
from kmor import kmor
X = np.array([
[1,0,0],
[0,1,0],
[0,0,1],
[0,0,100]
])
U = kmor(X, 1)
print(U) # [0,0,0,1]
The outliers are assigned to the extra cluster k.
Documentation
kmor(X, k, y, nc0, max_iteration, gamma)
| Parameter | Description |
|---|---|
| X | Your data. |
| k | Number of clusters. |
| y | Parameter for outlier detection. (default=3) Increase this to make outlier removal subtle. |
| nc0 | Maximum percentage of your data that can be assigned to outlier cluster. (default=0.1) |
| max_iteration | Maximum number of iterations. |
| gamma | Used to check the convergence. |
References
[1] Gan, Guojun, and Michael Kwok-Po Ng. "K-means clustering with outlier removal." Pattern Recognition Letters 90 (2017): 8-14.
1 maintainer
Release files for kmor 1.0.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| kmor-1.0.7.tar.gz | 2.7 kB | Details |
Release files / kmor-1.0.7.tar.gz
| Download URL | kmor-1.0.7.tar.gz |
|---|---|
| Size | 2.7 kB |
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|
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