Section 4.8.0.1 - Basic Writeup - Chandola,Banarjee,Kumar - Anomaly Detection: A Survey
Section 4.8.0.2 - Detailed Writeup - Goldstein,Uchida - A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data
Section 4.8.1.0 - Basic Writeup - Chandola,Banarjee,Kumar - Anomaly Detection: A Survey
Section 4.8.1.1 - Nearest Neighbor (Proximity) Based
Section 4.8.1.1.1 - Global Detection
Section 4.8.1.1.1.1 - k-NN Global Anomaly Detection
Section 4.8.1.1.2 - Local Detection
Section 4.8.1.1.2.1 - Density Based Approaches
Section 4.8.1.1.2.1.1 - Local Outlier Factor (LOF)
Section 4.8.1.1.2.1.2 - Connectivity-Based Outlier Factor (COF)
Section 4.8.1.1.2.1.3 - Influenced Outlierness (INFLO)
Section 4.8.1.1.2.1.4 - Local Outlier Probability (LoOP)
Section 4.8.1.1.2.1.5 - Local Correlation Integral (LOCI)
Section 4.8.1.1.2.1.5.0 - Basic Writeup - papadimitrious,Kitagawa,Gibbons,Faloutsos - LOCI: Fast Outlier Detection Using Local Correlation Integral
Section 4.8.1.1.2.1.5.1 - Approximate Local Correlation Integral (aLOCI)
Section 4.8.1.1.2.2 - Distance Based Approaches
Section 4.8.1.1.2.2.0 - Basic Writeup - Kriegel,Kroger,Zimek - Outlier Detection Techniques
Section 4.8.1.1.2.2.1 - Index Based
Section 4.8.1.1.2.2.2 - Nested Loop Based
Section 4.8.1.1.2.2.3 - Grid Based
Section 4.8.1.1.2.3 - High Dimensional Approaches
Section 4.8.1.1.2.3.1 - Angle Based Outlier Degree (ABOD)
Section 4.8.1.1.2.3.2 - Grid-based subspace outlier detection
Section 4.8.1.1.2.3.3 - Subspace Outlier Detection (SOD)
Section 4.8.1.1.2.4 - Depth Based Approaches
Section 4.8.1.1.2.4.1 - ISODEPTH
Section 4.8.1.1.2.4.2 - Fast Depth Contours (FDC)
Section 4.8.1.1.2.5 - Deviation Based Approaches
Section 4.8.1.2 - Cluster Based
Section 4.8.1.2.1 - Global Detection
Section 4.8.1.2.1.1 - Cluster-Based Local Outlier Factor (CBLOF/ uCBLOF)
Section 4.8.1.2.2 - Local Detection
Section 4.8.1.2.2.1 - Local Density Cluster-based Outlier Factor (LDCOF)
Section 4.8.1.2.2.2 - Clustering-based Multivariate Gaussian Outlier Score (CMGOS)
Section 4.8.1.2.2.3 - K-Means Clustering Based
Section 4.8.1.2.2.4 - Fuzzy C-Means (FCM) Based
Section 4.8.1.2.2.5 - Unsupervised Niche Clustering
Section 4.8.1.2.2.6 - Expectation-Maximization Meta Algorithm
Section 4.8.1.3 - Statistical Based
Section 4.8.1.3.1 - Histogram-based Outlier Score (HBOS)
Section 4.8.1.4 - Subspace Based
Section 4.8.1.4.1 - Robust Principal Component Analysis (rPCA)
Section 4.8.1.4.2 - Clustering-based Multivariate Gaussian Outlier Score (CMGOS)
Section 4.8.1.4.3 - Subspace Outlier Detection (SOD)
Section 4.8.1.5 - Classifier Based
Section 4.8.1.5.1 - One-Class Support Vector Machine
Section 4.8.1.6 - Neural Network Based
Section 4.8.1.6.1 - Self Organizing Map (SOM) Based
Section 4.8.1.6.1.0 - Basic Writeup - Stefanovic,Kurosava - Outlier Detection In Self-Organizing Maps and Their Quality Estimation
Section 4.8.1.6.1.1 - Self Organizing Map (SOM) using Particle Swarm Optimization
Section 4.8.1.6.2 - Adaptive Resonance Theory Based
Section 4.8.1.7 - Rank Based
Section 4.8.1.7.0 - Basic Writeup - Patel,Shah - A Survey of Anomalies Detection Using Density Based-Rank Based Outlier Detection Methods
Section 4.8.1.7.1 - Rank Based Detection Algorithm (RBDA)
Section 4.8.1.7.2 - Rank with Average Distance Algorithm (RADA)
Section 4.8.1.7.3 - Outlier Detection Using Modified Ranks (ODMR)
Section 4.8.2.1 - K -Nearest Neighbor (k-NN)
Section 4.8.2.2 - Bayesian Network (BN)
Section 4.8.2.3 - Supervised Neural Networks
Section 4.8.2.4 - Decision Tree
Section 4.8.2.5 - Support Vector Machine (SVM)
