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Authors: Tianshu Yang 1 ; Nicolas Pasquier 2 and Frederic Precioso 2

Affiliations: 1 Université Côte d’Azur, CNRS, I3S, France, Amadeus, Sophia-Antipolis, France ; 2 Université Côte d’Azur, CNRS, I3S, France

ISBN: 978-989-758-440-4

ISSN: 2184-285X

Keyword(s): Ensemble Clustering, Consensus Clustering, Closed Sets, Multi-level Clustering, Semi-supervised Learning, Amadeus Revenue Management, Revenue Accounting, Anomaly Corrections.

Abstract: We present a semi-supervised ensemble clustering framework for identifying relevant multi-level clusters, regarding application objectives, in large datasets and mapping them to application classes for predicting the class of new instances. This framework extends the MultiCons closed sets based multiple consensus clustering approach but can easily be adapted to other ensemble clustering approaches. It was developed to optimize the Amadeus Revenue Management application. Revenue Accounting in travel industry is a complex task when travels include several transportations, with associated services, performed by distinct operators and on geographical areas with different taxes and currencies for example. Preliminary results show the relevance of the proposed approach for the automation of Amadeus Revenue Management workflow anomaly corrections.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Yang, T.; Pasquier, N. and Precioso, F. (2020). Ensemble Clustering based Semi-supervised Learning for Revenue Accounting Workflow Management. In Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA, ISBN 978-989-758-440-4 ISSN 2184-285X, pages 283-293. DOI: 10.5220/0009883802830293

@conference{data20,
author={Tianshu Yang. and Nicolas Pasquier. and Frederic Precioso.},
title={Ensemble Clustering based Semi-supervised Learning for Revenue Accounting Workflow Management},
booktitle={Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA,},
year={2020},
pages={283-293},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009883802830293},
isbn={978-989-758-440-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA,
TI - Ensemble Clustering based Semi-supervised Learning for Revenue Accounting Workflow Management
SN - 978-989-758-440-4
IS - 2184-285X
AU - Yang, T.
AU - Pasquier, N.
AU - Precioso, F.
PY - 2020
SP - 283
EP - 293
DO - 10.5220/0009883802830293

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