Deep　clustering,　as　an　important　research　topic　in　machine　learning　and　data　mining,　has　been　widely　applied　in　many　real-world　scenarios.　However,　existing　deep　clustering　methods　primarily　rely　on　implicit　optimization　objectives　such　as　contrastive　learning　or　reconstruction,　which　do　not　explicitly　enforce　cluster-level　discrimination.　This　limitation　restricts　their　ability　to　achieve　compact　intra-cluster　structures　and　distinct　inter-cluster　separations.　To　overcome　this　limitation,　we　propose　a　novel　unsupervised　discriminative　deep　clustering　(discDC)　method,　which　explicitly　integrates　cluster-level　discrimination　into　the　learning　process.　The　proposed　discDC　framework　projects　data　into　a　nonlinear　latent　space　with　compact　and　well-separated　cluster　representations.　It　explicitly　optimizes　clustering　objectives　by　minimizing　intra-cluster　discrepancy　and　maximizing　inter-cluster　discrepancy.　Additionally,　to　tackle　the　lack　of　label　information　in　unsupervised　scenarios,　we　introduce　a　confidence-driven　self-labeling　mechanism,　which　iteratively　derives　reliable　pseudo-labels　to　enhance　discriminative　analysis.　Extensive　experiments　on　five　benchmark　datasets　demonstrate　the　superiority　of　discDC　over　state-of-the-art　deep　clustering　approaches.　©　2025　Elsevier　Ltd