Outlier Analysis
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Beschreibung
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.
Provides all the fundamental algorithms for outlier analysis in great detail including those for advanced data types, including specific insights into when and why particular algorithms work effectively
Discusses the latest ideas in the field such as outlier ensembles, matrix factorization, kernel methods, and neural networks
Covers theoretical and practical aspects of outlier analysis including specific practical details for accurate implementation
Offers numerous illustrations and exercises for classroom teaching, including a solution manual
Provides all the fundamental algorithms for outlier analysis in great detail including those for advanced data types, including specific insights into when and why particular algorithms work effectively Discusses the latest ideas in the field such as outlier ensembles, matrix factorization, kernel methods, and neural networks Covers theoretical and practical aspects of outlier analysis including specific practical details for accurate implementation Offers numerous illustrations and exercises for classroom teaching, including a solution manual Includes supplementary material: sn.pub/extras Request lecturer material: sn.pub/lecturer-material
Autor*in
Charu C. Aggarwal
Themen in »Outlier Analysis«
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 Machine learning Matrix factorization
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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)
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