3 accepted papers
Clustering is commonly compared through leaderboards that collapse performance into a single aggregate ranking. Such summaries obscure why methods succeed, which data properties align with failure, and how conclusions shift under representation changes and realistic tuning constraints. We present CH…
Anomaly detection is a crucial task in data mining, focusing on identifying data points that deviate significantly from the main patterns in the data. This paper introduces Anomaly Detection by an Ensemble of Random Pairs of Hyperspheres (ADERH), a new isolation-based technique leveraging two key ob…
Detecting anomalies with limited supervision is challenging due to the scarcity of labeled anomalies, which often fail to capture the diversity of abnormal behaviors. We propose Weakly Supervised Anomaly Detection via Dual-Tailed Kernel (WSAD-DT), a novel framework that learns robust latent represen…