FMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series
This paper introduces FMMVCC, a Mamba-based deep clustering framework that combines state space sequence modeling with multi-view self-supervised learning to efficiently capture long-range temporal dependencies in univariate time series, achieving state-of-the-art performance across 15 benchmark datasets without requiring manual labels.