A multivariate extreme value theory approach to anomaly clustering and visualization.
Maël ChiapinoStéphan ClémençonVincent FeuillardAnne SabourinPublished in: Comput. Stat. (2020)
Keyphrases
- extreme value theory
- anomaly detection
- self organizing maps
- clustering algorithm
- clustering method
- k means
- mixed data
- categorical data
- multidimensional scaling
- generative topographic mapping
- unsupervised learning
- document clustering
- detecting anomalies
- exploratory analysis
- multivariate data
- multivariate normal
- multivariate time series
- data clustering
- graph theoretic
- hierarchical clustering
- data sets
- data mining
- visualization tool
- low dimensional
- data points
- pairwise
- neural network