| 书目名称 | Outlier Analysis | | 编辑 | Charu C. Aggarwal | | 视频video | | | 概述 | 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 effective | | 图书封面 |  | | 描述 | 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, neu | | 出版日期 | Textbook 2017Latest edition | | 关键词 | Outlier Analysis; Anomaly detection; Outlier detection; Novelty detection; Outlier ensembles; Temporal ou | | 版次 | 2 | | doi | https://doi.org/10.1007/978-3-319-47578-3 | | isbn_softcover | 978-3-319-83772-7 | | isbn_ebook | 978-3-319-47578-3 | | copyright | Springer International Publishing AG 2017 |
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发表于 2025-3-21 17:27:49
