2009 CategoryDetectionUsingHierarchi

• (Vatturi et al., 2009) ⇒ Pavan Vatturi, and Weng-Keen Wong. (2009). “Category Detection Using Hierarchical Mean Shift.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557112

Subject Headings:

Notes

• Categories and Subject Descriptors: H.3.3 Information Storage and Retrieval: Information Search and Retrieval — Clustering; G.3 Probability and Statistics: Nonparametric statistics.
• General Terms: Algorithms, Experimentation, Performance

Cited By

• http://scholar.google.com/scholar?q=%22Category+detection+using+hierarchical+mean+shift%22+2009
• http://scholar.google.com/scholar?cluster=4191923732184781248&as_sdt=0,5
• http://portal.acm.org/citation.cfm?doid=1557019.1557112&preflayout=flat#citedby

Quotes

Author Keywords

Keywords Clustering, Anomaly Detection, Category Detection, Mean Shift

Abstract

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 present a new approach to rare category detection based on hierarchical mean shift. In our approach, a hierarchy is created by repeatedly applying mean shift with an increasing bandwidth on the data. This hierarchy allows us to identify anomalies in the data set at different scales, which are then posed as queries to the user. The main advantage of this methodology over existing approaches is that it does not require any knowledge of the dataset properties such as the total number of categories or the the prior probabilities of the categories. Results on real-world data sets show that our hierarchical mean shift approach performs consistently better than previous techniques.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 CategoryDetectionUsingHierarchi	Pavan Vatturi Weng-Keen Wong			Category Detection Using Hierarchical Mean Shift		KDD-2009 Proceedings		10.1145/1557019.1557112		2009