Non-redundant Time-series Clustering

Traditional time-series clustering methods typically return a single grouping. However, especially with the growing size of time-series data, a single dataset can have more than one meaningful clustering. Therefore, we introduce a non-redundant time-series clustering approach that explicitly uncovers multiple distinct clusterings within the same temporal data. To achieve this, the architecture employs a single-encoder, multiple-decoders design and is trained with a consistency clustering loss. Each decoder is then responsible for a single individual clustering. By independently masking and routing latent representations to these distinct decoders, this design acts as a tool for interpretability. It allows researchers to reconstruct the temporal sequence from isolated embeddings using a dedicated decoder, enabling explicit understanding of the perspectives and structural patterns that drive each separate clustering criterion. Furthermore, we introduce new non-redundant time-series clustering datasets and also collect existing benchmarks that already have multiple labelings. This provides a foundation for future non-redundant time-series analysis. Our evaluation shows that our method outperforms other methods on these datasets.