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
Rezensionen / Stimmen
"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)
Auflage
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Zielgruppe
Editions-Typ
Illustrationen
13
65 s/w Abbildungen, 13 farbige Abbildungen
XXII, 466 p. 78 illus., 13 illus. in color.
Maße
Höhe: 260 mm
Breite: 183 mm
Dicke: 32 mm
Gewicht
ISBN-13
978-3-319-47577-6 (9783319475776)
DOI
10.1007/978-3-319-47578-3
Schweitzer Klassifikation
Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.D. in Operations Research from the Massachusetts Institute of Technology in 1996. He has published more than 300 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is author or editor of 15 books, including textbooks on data mining, recommender systems, and outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He has received several internal and external awards, including the EDBT Test-of-Time Award (2014) and the IEEE ICDM Research Contributions Award (2015). He has also served as program or general chair of many major conferences in data mining. He is a fellow of the SIAM, ACM, and the IEEE, for "contributions to knowledge discovery and data mining algorithms."
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.
