基本信息
- 原书名：Data Mining, Second Edition, Second Edition : Concepts and Techniques
- 原出版社： Morgan Kaufmann
内容简介
作译者
Jiawei Han 伊利诺伊大学厄巴纳-尚佩恩分校计算机科学系教授。由于在数据挖掘和数据库系统领域卓有成效的研究工作，他曾多次获得各种荣誉和奖励，其中包括2004年ACM SIGKDD颁发的创新奖。同时，他还是《ACM Transactions on Knowledge Discovery from Data》杂志的主编，以及《IEEE Transactions on Knowledge and Data Engineering》和《Data Mining and Knowledge Discovery》杂志的编委会成员。
Micheline Kamber 拥有加拿大康考迪亚大学计算机科学硕士学位，现在加拿大西蒙·弗雷泽大学从事博士后研究工作。
Micheline Kamber 拥有加拿大康考迪亚大学计算机科学硕士学位，现在加拿大西蒙·弗雷泽大学从事博士后研究工作。
目录
Foreword .
Preface
1 Introduction
1.1 What Motivated Data Mining? Why Is It Important?
1.2 So, What Is Data Mining?
1.3 Data Mining--On What Kind of Data?
1.4 Data Mining Functionalities?What Kinds of Patterns Can Be Mined?
1.5 Are All of the Patterns Interesting?
1.6 Classification of Data Mining Systems
1.7 Data Mining Task Primitives
1.8 Integration of a Data Mining System with a Database or Data Warehouse System
1.9 Major Issues in Data Mining
1.10 Summary
1.11 Exercises
1.12 Bibliographic Notes
2 Data Preprocessing
2.1 Why Preprocess the Data?
2.2 Descriptive Data Summarization
2.3 Data Cleaning
2.4 Data Integration and Transformation
Preface
1 Introduction
1.1 What Motivated Data Mining? Why Is It Important?
1.2 So, What Is Data Mining?
1.3 Data Mining--On What Kind of Data?
1.4 Data Mining Functionalities?What Kinds of Patterns Can Be Mined?
1.5 Are All of the Patterns Interesting?
1.6 Classification of Data Mining Systems
1.7 Data Mining Task Primitives
1.8 Integration of a Data Mining System with a Database or Data Warehouse System
1.9 Major Issues in Data Mining
1.10 Summary
1.11 Exercises
1.12 Bibliographic Notes
2 Data Preprocessing
2.1 Why Preprocess the Data?
2.2 Descriptive Data Summarization
2.3 Data Cleaning
2.4 Data Integration and Transformation
前言
本书第2版更新和改进了原本已十分丰富和全面的第1版内容，并增添了新的重要课题，例如挖掘流数据、挖掘社群网络和挖掘空间、多媒体和其他复杂数据。本书将是一本适用于数据挖掘和知识发现课程的优秀教材。
——Gregory Piatetsky-Shapiro，KDnuggets的总裁
本书第2版最完整、最全面地讲述了数据挖掘领域的重要知识和技术创新。相比内容已经相当全面的第1版，第2版展示了该领域的最新研究成果，例如挖掘流、时序数据和序列数据以及挖掘空间、多媒体、文本和Web数据。本书是数据挖掘和知识发现领域内的所有教师、研究人员、开发人员和用户都必读的一本书。
——Hans-Peter Kriegel，德国慕尼黑大学
我们产生和收集数据的能力正在快速增长。除了大多数商业、科学和政府事务的日益计算机化，数码相机、发布工具和条码的广泛应用也会产生数据。在数据收集方面，扫描的文本和图像平台、卫星遥感系统和国际互联网已经使我们的生活被巨大的数据量所包围。这种爆炸性的数据增长促使我们比以往更加迫切地需要新技术和自动化工具来帮助我们将这些数据转换为有用的信息和知识。
本书第1版曾被KDnuggets的读者评选为最受欢迎的数据挖掘专著，是一本可读性极佳的教材。它从数据库角度全面系统地介绍数据挖掘的基本概念、基本方法和基本技术以及数据挖掘的技术研究进展，重点关注其可行性、有用性、有效性和可量测性问题。但是，自第1版出版之后，数据挖掘领域的研究又取得了很大的进展，开发出了新的数据挖掘方法、系统和应用。第2版在这一方面进行了加强，增加了多个章节讲述最新的数据挖掘方法，以便能够挖掘出复杂类型的数据——包括流数据、序列数据、图结构数据、社群网络数据和多重关系数据。
本书适合作为高等院校计算机及相关专业高年级本科生的选修课教材，特别适合作为研究生的专业课教材，同时也可供从事数据挖掘研究和应用开发工作的相关人员作为必备的参考书。
本书主要特点
·全面实用地论述了从实际业务数据中抽取出的读者需要知道的概念和技术。
·更新并结合了来自读者的反馈、数据挖掘领域的技术变化以及统计和机器学习方面的更多资料。
·包含了许多算法和实现示例，全部以易于理解的伪代码编写，适用于实际的大规模数据挖掘项目。
——Gregory Piatetsky-Shapiro，KDnuggets的总裁
本书第2版最完整、最全面地讲述了数据挖掘领域的重要知识和技术创新。相比内容已经相当全面的第1版，第2版展示了该领域的最新研究成果，例如挖掘流、时序数据和序列数据以及挖掘空间、多媒体、文本和Web数据。本书是数据挖掘和知识发现领域内的所有教师、研究人员、开发人员和用户都必读的一本书。
——Hans-Peter Kriegel，德国慕尼黑大学
我们产生和收集数据的能力正在快速增长。除了大多数商业、科学和政府事务的日益计算机化，数码相机、发布工具和条码的广泛应用也会产生数据。在数据收集方面，扫描的文本和图像平台、卫星遥感系统和国际互联网已经使我们的生活被巨大的数据量所包围。这种爆炸性的数据增长促使我们比以往更加迫切地需要新技术和自动化工具来帮助我们将这些数据转换为有用的信息和知识。
本书第1版曾被KDnuggets的读者评选为最受欢迎的数据挖掘专著，是一本可读性极佳的教材。它从数据库角度全面系统地介绍数据挖掘的基本概念、基本方法和基本技术以及数据挖掘的技术研究进展，重点关注其可行性、有用性、有效性和可量测性问题。但是，自第1版出版之后，数据挖掘领域的研究又取得了很大的进展，开发出了新的数据挖掘方法、系统和应用。第2版在这一方面进行了加强，增加了多个章节讲述最新的数据挖掘方法，以便能够挖掘出复杂类型的数据——包括流数据、序列数据、图结构数据、社群网络数据和多重关系数据。
本书适合作为高等院校计算机及相关专业高年级本科生的选修课教材，特别适合作为研究生的专业课教材，同时也可供从事数据挖掘研究和应用开发工作的相关人员作为必备的参考书。
本书主要特点
·全面实用地论述了从实际业务数据中抽取出的读者需要知道的概念和技术。
·更新并结合了来自读者的反馈、数据挖掘领域的技术变化以及统计和机器学习方面的更多资料。
·包含了许多算法和实现示例，全部以易于理解的伪代码编写，适用于实际的大规模数据挖掘项目。
序言
We are deluged by data--scientific data, medical data, demographic data, financial data, and marketing data. People have no time to look at this data. Human attention has become the precious resource. So, we must find ways to automatically analyze the data, to automatically classify it, to automatically summarize it, to automatically discover and characterize trends in it, and to automatically flag anomalies. This is one of the most active and exciting areas of the database research community. Researchers in areas includ- ing statistics, visualization, artificial intelligence, and machine learning are contributing to this field. The breadth of the field makes it difficult to grasp the extraordinary progress over the last few decades. .
Six years ago, Jiawei Han's and Micheline Kamber's seminal textbook organized and presented Data Mining. It heralded a golden age of innovation in the field. This revision of their book reflects that progress; more than half of the references and historical notes are to recent work. The field has matured with many new and improved algorithms, and has broadened to indude many more datatypes: streams, sequences, graphs, time-series, geospatial, audio, images, and video. We are certainly not at the end of the golden age-- indeed research and commercial interest in data mining continues to grow--but we are all fortunate to have this modern compendium. ..
The book gives quick introductions to database and data mining concepts with particular emphasis on data analysis. It then covers in a chapter-by-chapter tour the con- cepts and techniques that underlie classification, prediction, association, and clustering. These topics are presented with examples, a tour of the best algorithms for each prob- lem class, and with pragmatic rules of thumb about when to apply each technique. The Socratic presentation style is both very readable and very informative. I certainly learned a lot from reading the first edition and got re-educated and updated in reading the second edition.
Jiawei Han and Micheline )(amber have been leading contributors to data mining research. This is the text they use with their students to bring them up to speed on thefield. The field is evolving very rapidly, but this book is a quick way to learn the basic ideas, and to understand where the field is today. I found it very informative and stimulating, and believe you will too. ...
Jim Gray
Microsoft Research
San FranCisco, CA, USA
Six years ago, Jiawei Han's and Micheline Kamber's seminal textbook organized and presented Data Mining. It heralded a golden age of innovation in the field. This revision of their book reflects that progress; more than half of the references and historical notes are to recent work. The field has matured with many new and improved algorithms, and has broadened to indude many more datatypes: streams, sequences, graphs, time-series, geospatial, audio, images, and video. We are certainly not at the end of the golden age-- indeed research and commercial interest in data mining continues to grow--but we are all fortunate to have this modern compendium. ..
The book gives quick introductions to database and data mining concepts with particular emphasis on data analysis. It then covers in a chapter-by-chapter tour the con- cepts and techniques that underlie classification, prediction, association, and clustering. These topics are presented with examples, a tour of the best algorithms for each prob- lem class, and with pragmatic rules of thumb about when to apply each technique. The Socratic presentation style is both very readable and very informative. I certainly learned a lot from reading the first edition and got re-educated and updated in reading the second edition.
Jiawei Han and Micheline )(amber have been leading contributors to data mining research. This is the text they use with their students to bring them up to speed on thefield. The field is evolving very rapidly, but this book is a quick way to learn the basic ideas, and to understand where the field is today. I found it very informative and stimulating, and believe you will too. ...
Jim Gray
Microsoft Research
San FranCisco, CA, USA
