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KDD
2009
ACM

Category detection using hierarchical mean shift

14 years 6 months ago
Category detection using hierarchical mean shift
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 an existing category or declare the query data point to belong to a previously undiscovered category. The goal of category detection is to bring to the user’s attention a representative data point from each category in the data in as few queries as possible. In a data set with imbalanced categories, the main challenge is in identifying the rare categories or anomalies; hence, the task is often referred to as rare category detection. We prese...
Pavan Vatturi, Weng-Keen Wong
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where KDD
Authors Pavan Vatturi, Weng-Keen Wong
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