Cite this work as (BibTex):
@techreport{TratBenderOvtcharova2023_1000155196,
author = {Trat, Martin and Bender, Janek and Ovtcharova, Jivka},
year = {2023},
title = {Sensitivity-Based Optimization of Unsupervised Drift Detection for Categorical Data Streams},
doi = {10.5445/IR/1000155196},
institution = {{Karlsruher Institut für Technologie (KIT)}},
issn = {2194-1629},
series = {KIT Scientific Working Papers},
keywords = {unsupervised conceptdriftdetection, data streammining, productiveartificialintelligence, categorical data processing},
pagetotal = {10},
language = {english},
volume = {208}
}
Implementation of an augmented version of Dino Ienco's algorithm CDCStream (Change Detection in Categorical Evolving Data Streams) (https://doi.org/10.1145/2554850.2554864).
Requirements
Please note that several requirements need to be fulfilled in order to run the software. See repository readme for details.
Acknowledgements
This software was developed at the FZI Research Center for Information Technology. The associated research was funded by the German Federal Ministry of Education and Research (grant number: 02K18D033) within the context of the project SEAMLESS.
1 maintainer
Metadata
1 maintainer
Release files for cdcstream 0.2.3
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| cdcstream-0.2.3-py3-none-any.whl | Python 3 | none | any | Details for cdcstream-0.2.3-py3-none-any.whl |
Total release size: 76.2 kB
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