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Learning Representations for Time Series Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| DTCR instead of USSL | time series clustering · UCR 36 datasets | average Rand Index: 0.7676 -> 0.7714 | No clear effect |
| DTCR instead of DTC | time series clustering · UCR 36 datasets | average Rand Index: 0.6238 -> 0.7714 | Helped |
| DTCR instead of DTC | time series clustering · StarLightCurves | NMI: 0.6072 -> 0.6731 | Helped |
| add K-means spectral relaxation loss (ablation) | DTCR · UCR 36 datasets | Rand Index: 0.6970 -> 0.9638 (ECGFiveDays) | Helped |
| add fake-sample auxiliary classification loss (ablation) | DTCR · UCR 36 datasets | Rand Index: 0.5623 -> 0.9638 (ECGFiveDays) | Helped |
| keep reconstruction loss when K-means guidance is wrong (ablation) | DTCR · SonyAIBORobotSurface | Rand Index: 0.5062 -> 0.6165 | Helped |
Closest papers by meaning
- DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach (NeurIPS 2024 · similarity 0.85)
- Kernel Representation Learning for Time Sequence: Algorithm, Theory, and Applications (AAAI 2025 · similarity 0.84)
- T-Rep: Representation Learning for Time Series using Time-Embeddings (ICLR 2024 · similarity 0.84)
- Deep Temporal Graph Clustering (ICLR 2024 · similarity 0.83)
- Cross-Domain Contrastive Learning for Time Series Clustering (AAAI 2024 · similarity 0.83)
- TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation (ICML 2025 · similarity 0.83)
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