On explainability of cluster prototypes with rough sets: A case study in the FMCG market

• Autorzy: Grzegorowski Marek , Janusz Andrzej , Marcinowski Łukasz , Skowron Andrzej , Ślęzak Dominik , Śliwa Grzegorz

Identyfikatory

DOI

10.61822/amcs-2025-0002

• Języki publikacji: EN

Abstrakty

EN

Despite the growing popularity of machine learning (ML), such solutions are often incomprehensible to employees and difficult to control. Addressing this issue, we discuss some essential problems of explainable ML applications in the fast-moving consumer goods (FMCG) market. This research puts forward a new approach to effective supply management by utilizing rough sets (RST), distance-based clustering, and dimensionality reduction techniques. In the presented case study, we aim to reduce the work done by experts by applying a single delivery plan to many similar points of sale (PoS). We achieve this objective by clustering vending machines based on historical sales patterns. To verify the feasibility of such an approach, we performed a series of experiments related to demand prediction on two data representations with various clustering techniques. The conducted experiments confirmed that, without losing quality in terms of MAE and RMSE, we could operate on PoS in an aggregate manner, thus reducing the workload of preparing delivery plans.

Słowa kluczowe

EN

RST; clustering technique; PCA; UMAP; XAI; LLM; TRISM; FMCG; supply management

PL

RST; technika klasteryzacyjna; PCA; UMAP; XAI; LLM; TRISM; FMCG; zarządzanie dostawami

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2025
• Tom: Vol. 35, no. 1
• Strony: 19--31
• Opis fizyczny: Bibliogr. 28 poz., rys., tab., wykr.

Twórcy

autor

Grzegorowski Marek

• Institute of Informatics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland

autor

Janusz Andrzej

• Institute of Informatics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland
• School of Information Systems, Queensland University of Technology, Gardens Point Campus, Brisbane, Australia

autor

Marcinowski Łukasz

• FitFood, Solskiego 11/28, 31-216 Cracow, Poland

autor

Skowron Andrzej

• Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland

autor

Ślęzak Dominik

• Institute of Informatics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland
• QED Software, ul. Mazowiecka 11/49, 00-052 Warsaw, Poland

autor

Śliwa Grzegorz

• FitFood, Solskiego 11/28, 31-216 Cracow, Poland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).