描述
开 本: 16开纸 张: 胶版纸包 装: 平装是否套装: 否国际标准书号ISBN: 9787111316701丛书名: 经典原版书库
内容简介
本书全面介绍了数据挖掘的理论和方法，着重介绍如何用数据挖掘知识解决各种实际问题，涉及学科领域众多，适用面广。书中涵盖5个主题：数据、分类、关联分析、聚类和异常检测。除异常检测外，每个主题都包含两章：前面一章讲述基本概念、代表性算法和评估技术，后面一章较深入地讨论高级概念和算法。目的是使读者在透彻地理解数据挖掘基础的同时，还能了解更多重要的高级主题。.包含大量的图表、综合示例和丰富的习题。·不需要数据库背景。只需要很少的统计学或数学背景知识。·网上配套教辅资源丰富，包括PPT、习题解答、数据集等。
目 录
Preface
1 Introduction
1.1 What Is Data Mining?
1.2 Motivating Challenges
1.3 The Origins of Data Mining
1.4 Data Mining Tasks
1.5 Scope and Organization of the Book
1.6 Bibliographic Notes
1.7 Exercises
2 Data
2.1 Types of Data
2.1.1 Attributes and Measurement
2.1.2 Types of Data Sets
2.2 Data Quality
2.2.1 Measurement and Data Collection Issues
2.2.2 Issues Related to Applications
2.3 Data Preprocessing
2.3.1 Aggregation
2.3.2 Sampling
2.3.3 Dimensionality Reduction
2.3.4 Feature Subset Selection
2.3.5 Feature Creation
2.3.6 Discretization and Binarization
2.3.7 Variable Transformation
2.4 Measures of Similarity and Dissimilarity
2.4.1 Basics
2.4.2 Similarity and Dissimilarity between Simple Attributes.
2.4.3 Dissimilarities between Data Objects
2.4.4 Similarities between Data Objects
2.4.5 Examples of Proximity Measures
2.4.6 Issues in Proximity Calculation
2.4.7 Selecting the Right Proximity Measure
2.5 Bibliographic Notes
2.6 Exercises
3 Exploring Data
3.1 The Iris Data Set
3.2 Summary Statistics
3.2.1 Frequencies and the Mode
3.2.2 Percentiles
3.2.3 Measures of Location: Mean and Median
3.2.4 Measures of Spread: Range and Variance
3.2.5 Multivariate Summary Statistics
3.2.6 Other Ways to Summarize the Data
3.3 Visualization
3.3.1 Motivations for Visualization
3.3.2 General Concepts
3.3.3 Techniques
3.3.4 Visualizing Higher-Dimensional Data
3.3.5 Do’s and Don’ts
3.4 OLAP and Multidimensional Data Analysis
3.4.1 Representing Iris Data as a Multidimensional Array
3.4.2 Multidimensional Data: The General Case
3.4.3 Analyzing Multidimensional Data
3.4.4 Final Comments on Multidimensional Data Analysis
3.5 Bibliographic Notes
3.6 Exercises
Classification:
4 Basic Concepts, Decision Trees, and Model Evaluation
4.1 Preliminaries
4.2 General Approach to Solving a Classification Problem
4.3 Decision Tree Induction
4.3.1 How a Decision Tree Works
4.3.2 How to Build a Decision Tree
4.3.3 Methods for Expressing Attribute Test Conditions .
4.3.4 Measures for Selecting the Best Split
4.3.5 Algorithm for Decision Tree Induction
4.3.6 An Example: Web Robot Detection
4.3.7 Characteristics of Decision Tree Induction
4.4 Model Overfitting
4.4.1 Overfitting Due to Presence of Noise
4.4.2 Overfitting Due to Lack of Representative Samples .
4.4.3 Overfitting and the Multiple Comparison Procedure
4.4.4 Estimation of Generalization Errors
4.4.5 Handling Overfitting in Decision Tree Induction . .
4.5 Evaluating the Performance of a Classifier
4.5.1 Holdout Method
4.5.2 Random Subsampling
4.5.3 Cross-Validation
4.5.4 Bootstrap
4.6 Methods for Comparing Classifiers
4.6.1 Estimating a Confidence Interval for Accuracy
4.6.2 Comparing the Performance of Two Models
4.6.3 Comparing the Performance of Two Classifiers
4.7 Bibliographic Notes
4.8 Exercises
5 Classification: Alternative Techniques
6 Association Analysis: Basic Concepts and Algorithms
1 Introduction
1.1 What Is Data Mining?
1.2 Motivating Challenges
1.3 The Origins of Data Mining
1.4 Data Mining Tasks
1.5 Scope and Organization of the Book
1.6 Bibliographic Notes
1.7 Exercises
2 Data
2.1 Types of Data
2.1.1 Attributes and Measurement
2.1.2 Types of Data Sets
2.2 Data Quality
2.2.1 Measurement and Data Collection Issues
2.2.2 Issues Related to Applications
2.3 Data Preprocessing
2.3.1 Aggregation
2.3.2 Sampling
2.3.3 Dimensionality Reduction
2.3.4 Feature Subset Selection
2.3.5 Feature Creation
2.3.6 Discretization and Binarization
2.3.7 Variable Transformation
2.4 Measures of Similarity and Dissimilarity
2.4.1 Basics
2.4.2 Similarity and Dissimilarity between Simple Attributes.
2.4.3 Dissimilarities between Data Objects
2.4.4 Similarities between Data Objects
2.4.5 Examples of Proximity Measures
2.4.6 Issues in Proximity Calculation
2.4.7 Selecting the Right Proximity Measure
2.5 Bibliographic Notes
2.6 Exercises
3 Exploring Data
3.1 The Iris Data Set
3.2 Summary Statistics
3.2.1 Frequencies and the Mode
3.2.2 Percentiles
3.2.3 Measures of Location: Mean and Median
3.2.4 Measures of Spread: Range and Variance
3.2.5 Multivariate Summary Statistics
3.2.6 Other Ways to Summarize the Data
3.3 Visualization
3.3.1 Motivations for Visualization
3.3.2 General Concepts
3.3.3 Techniques
3.3.4 Visualizing Higher-Dimensional Data
3.3.5 Do’s and Don’ts
3.4 OLAP and Multidimensional Data Analysis
3.4.1 Representing Iris Data as a Multidimensional Array
3.4.2 Multidimensional Data: The General Case
3.4.3 Analyzing Multidimensional Data
3.4.4 Final Comments on Multidimensional Data Analysis
3.5 Bibliographic Notes
3.6 Exercises
Classification:
4 Basic Concepts, Decision Trees, and Model Evaluation
4.1 Preliminaries
4.2 General Approach to Solving a Classification Problem
4.3 Decision Tree Induction
4.3.1 How a Decision Tree Works
4.3.2 How to Build a Decision Tree
4.3.3 Methods for Expressing Attribute Test Conditions .
4.3.4 Measures for Selecting the Best Split
4.3.5 Algorithm for Decision Tree Induction
4.3.6 An Example: Web Robot Detection
4.3.7 Characteristics of Decision Tree Induction
4.4 Model Overfitting
4.4.1 Overfitting Due to Presence of Noise
4.4.2 Overfitting Due to Lack of Representative Samples .
4.4.3 Overfitting and the Multiple Comparison Procedure
4.4.4 Estimation of Generalization Errors
4.4.5 Handling Overfitting in Decision Tree Induction . .
4.5 Evaluating the Performance of a Classifier
4.5.1 Holdout Method
4.5.2 Random Subsampling
4.5.3 Cross-Validation
4.5.4 Bootstrap
4.6 Methods for Comparing Classifiers
4.6.1 Estimating a Confidence Interval for Accuracy
4.6.2 Comparing the Performance of Two Models
4.6.3 Comparing the Performance of Two Classifiers
4.7 Bibliographic Notes
4.8 Exercises
5 Classification: Alternative Techniques
6 Association Analysis: Basic Concepts and Algorithms
在线试读
Pang.Ning Tan现为密歇根州立大学计算机与工程系助理教授，主要教授数据挖掘、数据库系统等课程。他的研究主要关注于为广泛的应用（包括医学信息学、地球科学、社会网络、Web挖掘和计算机安全）开发适用的数据挖掘算法。
Michael Steinbach拥有明尼苏达大学数学学士学位、统计学硕士学位和计算机科学博士学位，现为明尼苏达大学双城分校计算机科学与工程系助理研究员。
Vipin Kumar现为明尼苏达大学计算机科学与工程系主任和William Norris教授。1 988年至2005年。他曾担任美国陆军高性能计算研究中心主任。
Michael Steinbach拥有明尼苏达大学数学学士学位、统计学硕士学位和计算机科学博士学位，现为明尼苏达大学双城分校计算机科学与工程系助理研究员。
Vipin Kumar现为明尼苏达大学计算机科学与工程系主任和William Norris教授。1 988年至2005年。他曾担任美国陆军高性能计算研究中心主任。
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