CompreX

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A parameter-free anomaly detection using pattern-based compression

CompreX 최신버젼 다운로드

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# Comprex

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A parameter-free anomaly detection using pattern-based compression
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Anomaly detection as one of the tasks in unsupervised learning is a hard task by itself, and adding categorical data which most of the algorithms requires encoding them makes it harder.

When dealing with high dimensional categorical data, features which have +1 million different values (which is my case) encoding becomes impractical. While researching for best approaches for dealing with this kind of datasets, I found **CompreX** [1] approach the most intuitive way to deal with the data by encode them using *shannon entropy* and *MDL (Minimum Description Length)*.

[1]: ./resources/fast-anomaly.pdf	"L. Akoglu, H. Tong, J. Vreeken, and C. Faloutsos. Fast and reliable anomaly detection incategorical data. 2012."

## Dependencies

1. Numpy
2. Pandas
3. Scikit-learn

## Roadmap

- [x] Initial implementation, v1.0.0
- [ ] Add tests
- [ ] Add `Docstrings`
- [ ] Publish documentation 
- [ ] Finish Scikit-learn style api compatibility issues
- [ ] Upload to PyPi

## Reference

[1] L. Akoglu, H. Tong, J. Vreeken, and C. Faloutsos. Fast and reliable anomaly detection incategorical data. 2012. ([read here](./resources/fast-anomaly.pdf))

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