Fast and Exact Outlier Detection in Metric Spaces
Daichi Amagata, Makoto Onizuka, Takahiro Hara
Abstract
Distance-based outlier detection is widely adopted in many fields, e.g., data mining and machine learning, because it is unsupervised, can be employed in a generic metric space, and does not have any assumptions of data distributions. Data mining and machine learning applications face a challenge of dealing with large datasets, which requires efficient distance-based outlier detection algorithms. Due to the popularization of computational environments with large memory, it is possible to build a main-memory index and detect outliers based on it, which is a promising solution for fast distance-based outlier detection.
Topics & Concepts
Anomaly detectionOutlierComputer scienceMetric (unit)Artificial intelligenceFace (sociological concept)Data miningPattern recognition (psychology)Machine learningEngineeringOperations managementSocial scienceSociologyAnomaly Detection Techniques and ApplicationsNetwork Security and Intrusion DetectionImbalanced Data Classification Techniques