The following pages link to LOF (Q31141):
Displaying 50 items.
- Loda: lightweight on-line detector of anomalies (Q298359) (← links)
- Weighted relaxed support vector machines (Q513634) (← links)
- Outlier detection for simple default theories (Q622112) (← links)
- Unsupervised interaction-preserving discretization of multivariate data (Q736501) (← links)
- A decomposition of the outlier detection problem into a set of supervised learning problems (Q747273) (← links)
- Clustering and outlier detection using isoperimetric number of trees (Q898224) (← links)
- A framework of irregularity enlightenment for data pre-processing in data mining (Q970169) (← links)
- An initialization method for the \(K\)-means algorithm using neighborhood model (Q980040) (← links)
- Semi-supervised outlier detection based on fuzzy rough \(c\)-means clustering (Q982921) (← links)
- Finding anomalous periodic time series (Q1009324) (← links)
- Case-based reasoning: the search for similar solutions and identification of outliers (Q1635045) (← links)
- Point process models for novelty detection on spatial point patterns and their extremes (Q1662930) (← links)
- One class proximal support vector machines (Q1669785) (← links)
- Commentary: A decomposition of the outlier detection problem into a set of supervised learning problems (Q1689575) (← links)
- Expected similarity estimation for large-scale batch and streaming anomaly detection (Q1689600) (← links)
- A new non-parametric detector of univariate outliers for distributions with unbounded support (Q1693607) (← links)
- Detecting anomaly collections using extreme feature ranks (Q1715864) (← links)
- Mining outlying aspects on numeric data (Q1715883) (← links)
- Discovering outlying aspects in large datasets (Q1741290) (← links)
- Outlying property detection with numerical attributes (Q1741295) (← links)
- Outlier detection using binary decision diagrams (Q1741310) (← links)
- Anomaly detection in spatiotemporal data via regularized non-negative tensor analysis (Q1741411) (← links)
- A precise ranking method for outlier detection (Q1750049) (← links)
- Initialization of \(K\)-modes clustering using outlier detection techniques (Q1750620) (← links)
- Dimensionality reduction for density ratio estimation in high-dimensional spaces (Q1784536) (← links)
- An improved semisupervised outlier detection algorithm based on adaptive feature weighted clustering (Q1793446) (← links)
- Mining blackhole and volcano patterns in directed graphs: a general approach (Q1945010) (← links)
- Mass estimation (Q1945021) (← links)
- Quantum speed-up for unsupervised learning (Q1945031) (← links)
- On cluster tree for nested and multi-density data clustering (Q1957875) (← links)
- Model-based exception mining for object-relational data (Q1987183) (← links)
- A distributed algorithm for the cluster-based outlier detection using unsupervised extreme learning machines (Q1992535) (← links)
- Smoothed self-organizing map for robust clustering (Q1999170) (← links)
- Homophily outlier detection in non-IID categorical data (Q2036755) (← links)
- Privacy preserving anomaly detection based on local density estimation (Q2038743) (← links)
- A critical overview of outlier detection methods (Q2065968) (← links)
- An empirical comparison between stochastic and deterministic centroid initialisation for K-means variations (Q2071340) (← links)
- Optimised one-class classification performance (Q2102347) (← links)
- Adversarially learned one-class novelty detection with confidence estimation (Q2126269) (← links)
- ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks (Q2127247) (← links)
- Unsupervised anomaly detection in multivariate time series with online evolving spiking neural networks (Q2163196) (← links)
- A survey of outlier detection in high dimensional data streams (Q2172855) (← links)
- Anomaly detection with inexact labels (Q2203336) (← links)
- Multivariate outlier detection in applied data analysis: global, local, compositional and cellwise outliers (Q2214956) (← links)
- Large scale anomaly detection in mixed numerical and categorical input spaces (Q2215134) (← links)
- On normalization and algorithm selection for unsupervised outlier detection (Q2218408) (← links)
- Anomaly detection in scientific data using joint statistical moments (Q2220587) (← links)
- A spatial filtering inspired three-way clustering approach with application to outlier detection (Q2237120) (← links)
- A coarse-to-fine approach for intelligent logging lithology identification with extremely randomized trees (Q2238076) (← links)
- AURORA: A Unified fRamework fOR Anomaly detection on multivariate time series (Q2238333) (← links)
