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Communication Dans Un Congrès Année : 2020

Ensemble Clustering Based Semi-Supervised Learning for Revenue Accounting Workflow Management

Résumé

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.

Dates et versions

hal-02540832 , version 1 (12-04-2020)

Identifiants

Citer

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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