In data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. — Wikipedia.
This post contents:
Outlier Analysis by Charu C. Aggarwal.
Outlier Ensembles by Charu C. Aggarwal, Saket Sathe.
Novelty detection: a review — part 1: statistical approaches
Novelty detection: a review — part 2:: neural network based approaches
A Survey of Outlier Detection Methodologies
LOF: Identifying Density-Based Local Outliers
LOF: Identifying Density-Based Local Outliers
LOCI: fast outlier detection using the local correlation integral
Revisiting Attribute Independence Assumption in Probabilistic Unsupervised Anomaly Detection
OPTICS-OF: Identifying Local Outliers
A local density-based approach for outlier detection
An efficient algorithm for distributed density-based outlier detection on big data
LoOP: Local Outlier Probabilities
A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data
Distance-based Outlier Detection in Data Streams
Rapid Distance-Based Outlier Detection via Sampling
Angle-based outlier detection in high-dimensional data
Scalable distance-based outlier detection over high-volume data streams
Fast mining of distance-based outliers in high-dimensional datasets
Distance-Based Outlier Detection on Uncertain Data
Distance-based outlier detection: consolidation and renewed bearing
Cluster-based outlier detection
Clustering-Based Outlier Detection Method
Efficient Clustering-Based Outlier Detection Algorithm for Dynamic Data Stream
An Outlier Detection Method Based on Clustering
Cluster Based Outlier Detection Algorithm for Healthcare Data
A Minimum Spanning Tree-Inspired Clustering-Based Outlier Detection Technique
Framework of Clustering-Based Outlier Detection
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