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Paper details
Number 1 - March 2025
Volume 35 - 2025
On explainability of cluster prototypes with rough sets: A case study in the FMCG market
Marek Grzegorowski, Andrzej Janusz, Łukasz Marcinowski, Andrzej Skowron, Dominik Ślęzak, Grzegorz Śliwa
Abstract
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.
Keywords
RST, clustering, PCA, UMAP, XAI, LLM, TRISM, FMCG, supply management