arXiv:2606. 11235v1 Announce Type: new Abstract: A key step in knowledge discovery is the evaluation of data mining results.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2606. 18209v1 Announce Type: new Abstract: Dataset distillation (DD) has emerged as a prominent approach in data centric machine learning, aiming to synthesize compact training sets for efficient training by compressing the information in large datasets into a small number of synthetic samples.
arXiv:2509. 05663v4 Announce Type: replace Abstract: Truly unsupervised approaches for time series anomaly detection are rare in the literature.
arXiv:2607. 03188v1 Announce Type: cross Abstract: Episode mining aims to extract subsequences of events that possess certain distinctive properties and constitute facts valuable to the user.
arXiv:2609.36686v1 Announce Type: new Abstract: Identifying the root cause of an anomaly among hundreds of sensors is critical for preventing safety incidents and costly downtime in complex monitored...
The paper investigates how AI‑generated data, treated as anomalies linked to main data points, influences batch decompositions and undersampling in random datasets. Using redundancy graphs and iterative methods, it derives bounds for the minimum size of a strongly dissimilar decomposition and shows a phase transition: when anomalies are few, the minimum size depends on main data, but beyond a threshold it is dominated by anomalies. Additionally, the authors present a size criticality result for strong similarity in randomly undersampled datasets, illustrated with categorical data examples where the overall space far exceeds the dataset size.
arXiv:2511. 03217v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel in generating fluent utterances but can lack reliable grounding in verified information.