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Ensemble Clustering Based Semi-Supervised Learning for Revenue Accounting Workflow Management: Amadeus Intellectual Property Invention Patent ID2326WW00 "Clustering Techniques for Revenue Accounting Error-Handling Automation" Defensive Paper

Tianshu Yang 1, 2 Nicolas Pasquier 1 Frédéric Precioso 1, 3
3 MAASAI - Modèles et algorithmes pour l’intelligence artificielle
CRISAM - Inria Sophia Antipolis - Méditerranée , UNS - Université Nice Sophia Antipolis (... - 2019), JAD - Laboratoire Jean Alexandre Dieudonné, Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
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 S.A.S revenue accounting workflow management. 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 workflow anomaly corrections.
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https://hal.archives-ouvertes.fr/hal-02540832
Contributor : Nicolas Pasquier <>
Submitted on : Sunday, April 12, 2020 - 1:31:05 AM
Last modification on : Thursday, February 18, 2021 - 3:29:48 AM

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Tianshu Yang, Nicolas Pasquier, Frédéric Precioso. Ensemble Clustering Based Semi-Supervised Learning for Revenue Accounting Workflow Management: Amadeus Intellectual Property Invention Patent ID2326WW00 "Clustering Techniques for Revenue Accounting Error-Handling Automation" Defensive Paper. DATA'2020 International Conference on Data Science, Technology and Applications (Acceptance Rate: 14%), Jul 2020, Paris, France. pp. 283-293, ⟨10.5220/0009883802830293⟩. ⟨hal-02540832⟩

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