Copertina

Evaluation of k-means time series clustering based on z-normalization and NP-Free

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Lee, Ming-Chang, Lin, Jia-Chun, Stolz, Volker
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

GAD: A Real-time Gait Anomaly Detection System with Online Adaptive Learning
di: Lee, Ming-Chang, et al.
Pubblicazione: (2024)

Investigating the Privacy Risk of Using Robot Vacuum Cleaners in Smart Environments
di: Ulsmaag, Benjamin, et al.
Pubblicazione: (2024)

LDTC: Lifelong deep temporal clustering for multivariate time series
di: Wang, Zhi, et al.
Pubblicazione: (2026)

On time series clustering with k-means
di: Holder, Christopher, et al.
Pubblicazione: (2024)

Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows
di: Baumgartner, David, et al.
Pubblicazione: (2026)

Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector
di: Toor, Aafan Ahmad, et al.
Pubblicazione: (2025)

RESTAD: REconstruction and Similarity based Transformer for time series Anomaly Detection
di: Ghorbani, Ramin, et al.
Pubblicazione: (2024)

ECATS: Explainable-by-design concept-based anomaly detection for time series
di: Ferfoglia, Irene, et al.
Pubblicazione: (2024)

Financial time series augmentation using transformer based GAN architecture
di: Podobiński, Andrzej, et al.
Pubblicazione: (2026)

Mini-Batch Kernel $k$-means
di: Jourdan, Ben, et al.
Pubblicazione: (2024)

Decomposition-based multi-scale transformer framework for time series anomaly detection
di: Zhang, Wenxin, et al.
Pubblicazione: (2025)

Improving Perturbation-based Explanations by Understanding the Role of Uncertainty Calibration
di: Decker, Thomas, et al.
Pubblicazione: (2025)

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations
di: Decker, Thomas, et al.
Pubblicazione: (2025)

Gradient Weight-normalized Low-rank Projection for Efficient LLM Training
di: Huang, Jia-Hong, et al.
Pubblicazione: (2024)

Comparative study of clustering models for multivariate time series from connected medical devices
di: Courrier, Violaine, et al.
Pubblicazione: (2023)

Accelerating the k-means++ Algorithm by Using Geometric Information
di: Corominas, Guillem Rodríguez, et al.
Pubblicazione: (2024)

Towards plausibility in time series counterfactual explanations
di: Kostrzewa, Marcin, et al.
Pubblicazione: (2026)

NPG-Muse: Scaling Long Chain-of-Thought Reasoning with NP-Hard Graph Problems
di: Wang, Yuyao, et al.
Pubblicazione: (2025)

FedSAUC: A Similarity-Aware Update Control for Communication-Efficient Federated Learning in Edge Computing
di: Lee, Ming-Lun, et al.
Pubblicazione: (2025)

CASA: CNN Autoencoder-based Score Attention for Efficient Multivariate Long-term Time-series Forecasting
di: Lee, Minhyuk, et al.
Pubblicazione: (2025)

Diffusion Attribution Score: Evaluating Training Data Influence in Diffusion Models
di: Lin, Jinxu, et al.
Pubblicazione: (2024)

Hierarchical clustering that takes advantage of both density-peak and density-connectivity
di: Zhu, Ye, et al.
Pubblicazione: (2018)

Hessian-Free Online Certified Unlearning
di: Qiao, Xinbao, et al.
Pubblicazione: (2024)

Routing-Free Mixture-of-Experts
di: Liu, Yilun, et al.
Pubblicazione: (2026)

Grey-informed neural network for time-series forecasting
di: Xie, Wanli, et al.
Pubblicazione: (2024)

Is Prompt Selection Necessary for Task-Free Online Continual Learning?
di: Park, Seoyoung, et al.
Pubblicazione: (2026)

Near-Optimal Algorithms for Constrained k-Center Clustering with Instance-level Background Knowledge
di: Guo, Longkun, et al.
Pubblicazione: (2024)

Wavelet Mixture of Experts for Time Series Forecasting
di: Zhou, Zheng, et al.
Pubblicazione: (2025)

ETTRL: Balancing Exploration and Exploitation in LLM Test-Time Reinforcement Learning Via Entropy Mechanism
di: Liu, Jia, et al.
Pubblicazione: (2025)

The Unseen Frontier: Pushing the Limits of LLM Sparsity with Surrogate-Free ADMM
di: Lee, Kwanhee, et al.
Pubblicazione: (2025)

LLM-ABBA: Understanding time series via symbolic approximation
di: Chen, Xinye, et al.
Pubblicazione: (2024)

Advancing time series completion via RFAMoE and MDFF
di: Zhang, Ci, et al.
Pubblicazione: (2025)

Foundation models for time series forecasting: Application in conformal prediction
di: Achour, Sami, et al.
Pubblicazione: (2025)

Evaluation of post-hoc interpretability methods in time-series classification
di: Turbé, Hugues, et al.
Pubblicazione: (2022)

Score-based Conditional Out-of-Distribution Augmentation for Graph Covariate Shift
di: Wang, Bohan, et al.
Pubblicazione: (2024)

Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off
di: Waseda, Futa, et al.
Pubblicazione: (2024)

Improving Node Representation by Boosting Target-Aware Contrastive Loss
di: Lin, Ying-Chun, et al.
Pubblicazione: (2024)

Physics-integrated generative modeling using attentive planar normalizing flow based variational autoencoder
di: Akhtar, Sheikh Waqas
Pubblicazione: (2024)

Canonical Variates in Wasserstein Metric Space
di: Li, Jia, et al.
Pubblicazione: (2024)

Task-Distributionally Robust Data-Free Meta-Learning
di: Hu, Zixuan, et al.
Pubblicazione: (2023)