Outlier Analysis (2nd Ed., 2nd ed. 2017)
Auteur : Aggarwal Charu C.
- Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods.
- Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data.
- Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner.
Date de parution : 12-2016
Ouvrage de 466 p.
17.8x25.4 cm
Date de parution : 05-2018
Ouvrage de 466 p.
17.8x25.4 cm
Thèmes d’Outlier Analysis :
Mots-clés :
Outlier Analysis; Anomaly detection; Outlier detection; Novelty detection; Outlier ensembles; Temporal outlier detection; Temporal anomaly detection; Network outlier detection; Spatial outliers; Streaming outlier detection; Text outliers; Artificial intelligence; Data mining; Machine learning; Matrix factorization