5 papers
Differentiable Interval Bottlenecks for Interpretable Anomaly Detection in Numerical Data
Lamine Diop, Marc Plantevit
Reconstruction-based anomaly detectors are accurate but opaque: a deep autoencoder flags a sample without telling a practitioner which feature ranges made it anomalous. We propose…
Witnesses Explain Anomalies
Lamine Diop
Unsupervised anomaly detection scores each point of an unlabelled, contaminated sample in a single pass, and increasingly must also explain why a point is flagged. Yet the dominant…
Efficiently Sampling Interval Patterns from Numerical Databases
Djawad Bekkoucha, Lamine Diop, Abdelkader Ouali +2
Pattern sampling has emerged as a promising approach for information discovery in large databases, allowing analysts to focus on a manageable subset of patterns. In this approach,…
RPS: A Generic Reservoir Patterns Sampler
Lamine Diop, Marc Plantevit, Arnaud Soulet
Efficient learning from streaming data is important for modern data analysis due to the continuous and rapid evolution of data streams. Despite significant advancements in stream p…
Scalable Sampling for High Utility Patterns
Lamine Diop, Marc Plantevit
Discovering valuable insights from data through meaningful associations is a crucial task. However, it becomes challenging when trying to identify representative patterns in quanti…