Category detection using hierarchical mean shift
Pavan Kumar Vatturi, Weng‐Keen Wong · 2009
Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine learning that can help address this issue using a ”human-in-the-loop”approach. In this interactive setting, the algorithm asks the user to label a query data point under