Multivariate Time-Series (MTS) clustering is essential for uncovering latent temporal pattern distributions, yet it is significantly challenged by the prevalence of redundant, low-discriminative information in real-world data. That is, redundancy refers to repetitive, meaningless temporal patterns, while low-discriminative information denotes components within representations that hinder the differentiation of sample differences. Such redundancy dilutes cluster-specific temporal signatures, thereby impairing clustering performance. Although mainstream approaches mitigate redundancy through static masking, they lack the ability to refine adaptively driven by training, limiting their focus to truly discriminative characteristics. To address this limitation, an Evolving Masking Representation Learning model for Multivariate Time-Series Clustering (EMTC) is proposed, adopting evolving masking that leverages model training to mask redundant timestamps while highlighting cluster-friendly temporal information through adaptive refinement and multi-endogenous view contrastive training for capturing localized nuances and global coherence. At its core is an Evolving Masking Mechanism via dynamic self-attention: the module observes multi-endogenous view representations to identify discriminative features across local perspectives, pinpointing timestamps critical for high-discriminability representations. These insights refine masking by suppressing redundant timestamps and enhancing inter-sample discriminability. Furthermore, a Consistency and Reconstruction Adjustment module enhances training stability by enforcing cross-view consistency and facilitating MEV reconstruction, ensuring the learning of robust representations. Comprehensive experiments conducted on 13 real-world benchmark datasets demonstrate that EMTC significantly outperforms state-of-the-art methods in both redundancy reduction and overall clustering performance.