Deep clustering aims to boost clustering performance by learning powerful representations via deep learning. Despite their superiority over conventional shallow algorithms, autoencoder-based methods are typically hindered by heavy dependencies on large datasets and computationally expensive pre-training phases. Moreover, they often struggle to learn representations that are sufficiently discriminative for complex clustering tasks. To bridge this gap, we introduce a novel discriminative clustering framework utilizing Siamese encoders. By jointly training a Siamese encoder and a discriminative learning module, our method simultaneously captures robust features from data augmentations and imposes intra-cluster compactness. This dual optimization yields highly discriminative representations, which obviates the necessity for pre-training while ensuring rapid convergence and high accuracy. Extensive experiments on multiple benchmarks validate the superiority of our approach over state-of-the-art baselines.