<p>In recent years, the increasing complexity of datasets has highlighted the limitations of traditional feature selection methods and motivated the search for more stable and robust alternatives. This study proposes a bootstrap-stabilized feature selection method based on the k-means clustering algorithm, where features are repeatedly grouped, and stable representative features are identified across multiple resamples. In the proposed approach, each feature is assigned to its nearest cluster center, and the feature closest to that center is selected as the representative, reducing sensitivity to random initialization and improving generalizability. The selected features are subsequently integrated into a Fuzzy Regression Function (FRF) model, forming a hybrid framework that combines interpretable feature selection with flexible fuzzy regression modeling. The method is evaluated on ten real-world datasets and compared with widely used feature selection techniques, including Correlation based Feature Selection (CFS), Recursive Feature Elimination (RFE), Genetic Algorithm (GA) based Feature Selection, LASSO, and Ridge regression. In the proposed framework, the number of feature clusters was primarily determined using a grid-search strategy, while the Silhouette Index (SI) and Davies Bouldin Index (DBI) were additionally considered as alternative cluster validity criteria for k-means based feature selection. These findings demonstrate that the proposed bootstrap-stabilized clustering approach achieved either superior or competitive performance across the ten datasets considered, highlighting its effectiveness and suitability for complex data environments.</p>