包邮数据挖掘导论(英文版)

1　Introduction　1
1.1　What Is Data Mining?　2
1.2　Motivating Challenges　3
1.3　The Origins of Data Mining　4
1.4　Data Mining Tasks　5
1.5　Scope and Organization of the Book　8
1.6　Bibliographic Notes　9
1.7　Exercises　12

2　Data　13
2.1　Types of Data　15
2.1.1　Attributes and Measurement　15
2.1.2　Types of Data Sets　20
2.2　Data Quality　25
2.2.1　Measurement and Data Collection Issues　26
2.2.2　Issues Related to Applications　31
2.3　Data Preprocessing　32
2.3.1　Aggregation　32
2.3.2　Sampling　34
2.3.3　Dimensionality Reduction　36
2.3.4　Feature Subset Selection　37
2.3.5　Feature Creation　39
2.3.6　Discretization and Binarization　41
2.3.7　Variable Transformation　45
2.4　Measures of Similarity and Dissimilarity　47
2.4.1　Basics　47
2.4.2　Similarity and Dissimilarity between Simple Attributes　49
2.4.3　Dissimilarities between Data Objects　50
2.4.4　Similarities between Data Objects　52
2.4.5　Examples of Proximity Measures　53
2.4.6　Issues in Proximity Calculation　58
2.4.7　Selecting the Right Proximity Measure　60
2.5　Bibliographic Notes　61
2.6　Exercises　64

3　Exploring Data　71
3.1　The Iris Data Set　71
3.2　Summary Statistics　72
3.2.1　Frequencies and the Mode　72
3.2.2　Percentiles　73
3.2.3　Measures of Location: Mean and Median　73
3.2.4　Measures of Spread: Range and Variance　75
3.2.5　Multivariate Summary Statistics　76
3.2.6　Other Ways to Summarize the Data　77
3.3　Visualization　77
3.3.1　Motivations for Visualization　77
3.3.2　General Concepts　78
3.3.3　Techniques　81
3.3.4　Visualizing Higher-Dimensional Data　90
3.3.5　Do's and Don'ts　94
3.4　OLAP and Multidimensional Data Analysis　95
3.4.1　Representing Iris Data as a Multidimensional Array　95
3.4.2　Multidimensional Data: The General Case　97
3.4.3　Analyzing Multidimensional Data　98
3.4.4　Final Comments on Multidimensional Data Analysis　101
3.5　Bibliographic Notes　102
3.6　Exercises　103

4　Classification: Basic Concepts, Decision Trees, and Model Evaluation　105
4.1　Preliminaries　105
4.2　General Approach to Solving a Classification Problem　107
4.3　Decision Tree Induction　108
4.3.1　How a Decision Tree Works　108
4.3.2　How to Build a Decision Tree　110
4.3.3　Methods for Expressing Attribute Test Conditions　112
4.3.4　Measures for Selecting the Best Split　114
4.3.5　Algorithm for Decision Tree Induction　119
4.3.6　An Example: Web Robot Detection　120
4.3.7　Characteristics of Decision Tree Induction　122
4.4　Model Overfitting　125
4.4.1　Overfitting Due to Presence of Noise　127
4.4.2　Overfitting Due to Lack of Representative Samples　129
4.4.3　Overfitting and the Multiple Comparison Procedure　129
4.4.4　Estimation of Generalization Errors　131
4.4.5　Handling Overfitting in Decision Tree Induction　134
4.5　Evaluating the Performance of a Classifier　135
4.5.1　Holdout Method　136
4.5.2　Random Subsampling　136
4.5.3　Cross-Validation　136
4.5.4　Bootstrap　137
4.6　Methods for Comparing Classifiers　137
4.6.1　Estimating a Confidence Interval for Accuracy　138
4.6.2　Comparing the Performance of　Two Models　139
4.6.3　Comparing the Performance of Two Classifiers　140
4.7　Bibliographic Notes　141
4.8　Exercises　144

5　Classification: Alternative Techniques　151
5.1　Rule-Based Classifier　151
5.1.1　How a Rule-Based Classifier Works　153
5.1.2　Rule-Ordering Schemes　154
5.1.3　How to Build a Rule-Based Classifier　155
5.1.4　Direct Methods for Rule Extraction　155
5.1.5　Indirect Methods for Rule Extraction　161
5.1.6　Characteristics of Rule-Based Classifiers　163
5.2　Nearest-Neighbor classifiers　163
5.2.1　Algorithm　165
5.2.2　Characteristics of Nearest-Neighbor Classifiers　165
5.3　Bayesian Classifiers　166
5.3.1　Bayes Theorem　166
5.3.2　Using the Bayes Theorem for Classification　168
5.3.3　Na?ve Bayes Classifier　169
5.3.4　Bayes Error Rate　175
5.3.5　Bayesian Belief Networks