具体描述
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, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.
作者简介
From the Back Cover
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.
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About the Author
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 undergraduatedegree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 andhis Ph.D. in Operations Research from the Massachusetts Institute of Technology in 1996.He has published more than 300 papers in refereed conferences andjournals, 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 commercialvalue of his patents, he has thrice been designated a MasterInventor at IBM. He has received several internal and externalawards, including the EDBT Test-of-Time Award (2014) andthe IEEE ICDM Research Contributions Award (2015). He has alsoserved as program or general chair of many major conferences in datamining. He is a fellow of the SIAM, ACM, and the IEEE, for “contributions to knowledgediscovery and data mining algorithms.”
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目录信息
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用户评价
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初读入迷，我立刻被作者那行云流水的叙事笔法所吸引住了。他的文字如同清澈的溪流，看似平缓，实则暗流涌动，总能在不经意间抛出一个极具洞察力的观点，让人猛地停下来，反复咀嚼。这种叙述的节奏感拿捏得极为精妙，既有学术论著的扎实与逻辑，却又避免了传统硬科学书籍的枯燥与晦涩。他擅长使用生活化的比喻来解释那些抽象的概念，仿佛他正坐在我对面，以一种极为耐心且充满热情的口吻，将那些复杂的理论层层剥开，露出其内核的本质。读完其中关于“范式转移”的章节后，我感觉自己对过去许多习以为常的认知框架产生了一种轻微的震颤，这是一种非常难得的体验——仿佛大脑被重新格式化了一遍，旧有的数据被清洗，新的连接正在快速建立。这种阅读体验的流畅与启发性，完全超出了我对一本专业书籍的预期，更像是一次与一位大师的深度对话。
这本书的封面设计简直是一场视觉盛宴，那种深邃的蓝色调搭配着跳跃的亮色粒子，仿佛在诉说着某种宇宙的奥秘，让人忍不住想一探究竟。翻开扉页，那种纸张特有的油墨香气扑鼻而来，立刻将我从现实的喧嚣中抽离出来，带入了一个充满智识探索的静谧空间。装帧的质感非常出色，每一页的裁切都精准得令人赞叹，看得出出版方在细节上是下了真功夫的。我尤其喜欢它内页的排版，字体大小适中，行间距留白得恰到好处，即便是长时间阅读也不会感到眼睛疲劳，这对于一本需要深度思考的书来说，无疑是加分项。这种对物理形态的精雕细琢，似乎也在暗示着内容本身的严谨与深度，让我对即将展开的阅读旅程充满了期待和敬意。它不仅仅是一本书，更像是一件值得珍藏的艺术品，放在书架上都散发着一股沉稳而高贵的气息，成功地在第一印象中就俘获了我这个外貌协会资深成员的心。
总而言之，这本书带给我的是一种持久而深刻的回味感，它不是那种读完就忘的快餐读物。合上书本的那一刻，我感受到的不是任务完成的轻松，而是一种仿佛刚刚经历了一场漫长而富有成效的远足后的满足感与疲惫感交织的情绪。书中的一些关键概念，已经像种子一样埋在了我的潜意识里，时不时地会在我处理日常工作或思考人生抉择时冒出来，成为一种全新的分析工具。我发现自己开始不自觉地用作者提供的视角去审视身边的事物，这种思维模式的迁移，才是真正衡量一本优秀书籍价值的最高标准。它不是简单地告诉你“是什么”，而是教会你“如何去看待”世界。这本书的价值，将在未来的很长一段时间内，持续地影响我的思考方式和决策过程，绝对是值得反复翻阅的案头经典。
我必须提及这本书在案例选取上的独到眼光。作者似乎拥有一个巨大的资料库，从中撷取出最具有代表性和冲击力的实例，用以佐证其核心论点。这些案例并非教科书式的标准范例，而是充满烟火气和争议性的真实情境，它们来自不同的领域，跨越了时间维度，这极大地增强了理论的可信度和普适性。每当理论阐述进入一个瓶颈期，作者总能适时地引入一个令人拍案叫绝的例子，瞬间点亮整个论述的维度，让抽象的理论具象化为鲜活的场景。我记得其中一则关于历史决策失误的分析，那种细节的捕捉和对人性弱点的洞察，细致到令人不寒而栗。它不仅仅是提供证据，更像是一种情景再现，让我仿佛置身于事件发生的现场，亲身感受决策者的困境与挣扎。这种对“活的知识”的追求，让这本书的价值远远超出了纯粹的理论探讨。
项目开头基本靠此书续命，范围广但浅，往深了挖就不行
重复读
读了1、2、9、10章，作者把异常检测的常用方法和思路做了一个综述。
项目开头基本靠此书续命，范围广但浅，往深了挖就不行
读了1、2、9、10章，作者把异常检测的常用方法和思路做了一个综述。
