About this book
This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories:
- 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.
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.
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
- Book Title Outlier Analysis
- DOI https://doi.org/10.1007/978-3-319-47578-3
- Copyright Information Springer International Publishing AG 2017
- Publisher Name Springer, Cham
- eBook Packages Computer Science Computer Science (R0)
- Hardcover ISBN 978-3-319-47577-6
- Softcover ISBN 978-3-319-83772-7
- eBook ISBN 978-3-319-47578-3
- Edition Number 2
- Number of Pages XXII, 466
- Number of Illustrations 65 b/w illustrations, 13 illustrations in colour
Data Mining and Knowledge Discovery
Statistics and Computing/Statistics Programs
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“This book presents an extensive coverage on outlier analysis from data mining and computer science perspective. Each chapter includes a detailed coverage of the topics, case studies, extensive bibliographic notes, a number of exercises, and the future direction of research in this field. The book is a good source for researchers also could be used as textbook in the related discipline.” (Morteza Marzjarani, Technometrics, Vol. 60 (2), 2018)