DFR: A Density-Based Fuzzy Rough Clustering Algorithm

Density-based clustering algorithms are widely used for discovering clusters of arbitrary shapes without prior knowledge, yet they encounter major challenges when dealing with fuzzy boundaries and ensuring scalability on complex datasets. To address these issues, we propose a novel density-based fuzzy rough clustering algorithm (DFR). First, a new definition of core points is introduced by incorporating fuzzy dominance relations, enabling more accurate identification of dense regions. Second, DFR decomposes the clustering process into independent expansion and clustering stages, with hyperparameters that enhance the flexibility and adaptability of the algorithm. Third, by integrating fuzzy rough set theory with Euclidean distance, the algorithm effectively handles boundary fuzziness and simultaneously improves stability and outlier detection. Experimental results on a wide range of synthetic and real-world datasets demonstrate that DFR consistently outperforms stateof- the-art clustering methods, particularly excelling in scenarios with fuzzy boundaries and high-dimensional data. Through these innovations, DFR achieves superior clustering accuracy, robustness, and interpretability, offering an efficient and reliable solution for complex data analysis.