Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been …
Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques …
In the statistics community, outlier detection for time series data has been studied for decades. Recently, with advances in hardware and software technology, there has been a …
Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in …
The collection of digital information by governments, corporations, and individuals has created tremendous opportunities for knowledge-and information-based decision making …
G Hulten, L Spencer, P Domingos - Proceedings of the seventh ACM …, 2001 - dl.acm.org
Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for …
RJ Bolton, DJ Hand - Statistical science, 2002 - projecteuclid.org
Fraud is increasing dramatically with the expansion of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each …
Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based …
F Provost, T Fawcett - Machine learning, 2001 - Springer
In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building robust …