RDSCUF

In My study, we evaluate the performance of the proposed clustering method across a wide range of publicly available datasets that represent different data modalities. Specifically, Jaffe, ExtendYaleB, and ORL are employed as facial image datasets to assess the method's capability in handling variations in facial expressions and lighting conditions. Mnistsc2000, Binary Alphadigits, and USPS serve as handwritten character datasets, providing diverse styles and stroke patterns to test robustness. Additionally, Iris and Coil20 are utilized to represent floral and object recognition tasks, respectively. This diverse selection ensures a comprehensive evaluation of clustering effectiveness across domains such as biometrics, character recognition, and object identification.