On explainability of cluster prototypes with rough sets: A case study in the FMCG market - Grzegorowski, Marek; Janusz, Andrzej; Marcinowski, Łukasz; Skowron, Andrzej; Ślęzak, Dominik; Śliwa, Grzegorz (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: RST; clustering; PCA; UMAP; XAI; LLM; TRISM; FMCG; supply management