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

Autorentext

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."

Klappentext

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.

  Inhalt

  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.