Buch, Englisch, 466 Seiten, Paperback, Format (B × H): 178 mm x 254 mm, Gewicht: 909 g
Buch, Englisch, 466 Seiten, Paperback, Format (B × H): 178 mm x 254 mm, Gewicht: 909 g
ISBN: 978-3-319-83772-7
Verlag: Springer International Publishing
- 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.
The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.
Zielgruppe
Graduate
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
An Introduction to Outlier Analysis.- Probabilistic Models for Outlier Detection.- Linear Models for Outlier Detection.- Proximity-Based Outlier Detection.- High-Dimension Outlier Detection.- Outlier Ensembles.- Supervised Outlier Detection.- Categorical, Text, and Mixed Attribute Data.- Time Series and Streaming Outlier Detection.- Outlier Detection in Discrete Sequences.- Spatial Outlier Detection.- Outlier Detection in Graphs and Networks.- Applications of Outlier Analysis.