《数据挖掘：实践机器学习工具与技术（第3版）》详解

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《数据挖掘：实践机器学习工具与技术》（第三版），是Ian H. Witten、Eibe Frank和Mark A. Hall合著的一本经典著作，隶属于摩根·考夫曼数据管理系统系列。本书的第三版在2011年1月20日由Finelybook出版，共有664页，详细介绍了数据挖掘领域的实用机器学习方法和技术。数据挖掘是一门交叉学科，它结合了统计学、计算机科学和人工智能，旨在从大量数据中发现有价值的信息和知识模式。本书提供了丰富的实践案例和理论框架，帮助读者理解和掌握数据挖掘的基本原理和各种算法，包括但不限于分类、回归、聚类、关联规则学习以及最近邻搜索等。作者们深入浅出地讲解了如何使用诸如Apriori、C4.5、决策树、神经网络、支持向量机等常见工具进行数据预处理、特征选择、模型构建和评估。在第三版中，作者们可能更新了对现代数据挖掘技术的介绍，例如深度学习的兴起及其在大数据处理中的应用，以及云计算和分布式计算如何提升数据挖掘的效率。此外，书中可能会涉及如何处理大规模数据集、如何处理缺失值和异常值、以及如何将数据挖掘技术应用于商业智能、推荐系统、社交网络分析等领域。书中还可能包含了关于评估数据挖掘模型性能的准则，如精确度、召回率、F1分数、AUC-ROC曲线等，并讨论了如何选择和调整参数以优化模型。同时，书中会强调数据隐私和伦理问题，尤其是在处理敏感信息时如何确保合规性和尊重用户权益。《数据挖掘：实践机器学习工具与技术》第三版为读者提供了一个全面而实用的数据挖掘学习平台，无论是在学术研究还是实际项目中，都能帮助读者有效地发掘隐藏在数据中的知识，推动业务决策和技术创新。

Figure 1.1 Rules for the contact lens data. 12

Figure 1.2 Decision tree for the contact lens data. 13

Figure 1.3 Decision trees for the labor negotiations data. 18

Figure 2.1 A family tree and two ways of expressing the sister-of relation. 43

Figure 2.2 ARFF ﬁle for the weather data. 53

Figure 2.3 Multi-instance ARFF ﬁle for the weather data. 55

Figure 3.1 A linear regression function for the CPU performance data. 62

Figure 3.2 A linear decision boundary separating Iris setosas from Iris

versicolors. 63

Figure 3.3 Constructing a decision tree interactively. 66

Figure 3.4 Models for the CPU performance data. 68

Figure 3.5 Decision tree for a simple disjunction. 69

Figure 3.6 The exclusive-or problem. 70

Figure 3.7 Decision tree with a replicated subtree. 71

Figure 3.8 Rules for the iris data. 74

Figure 3.9 The shapes problem. 76

Figure 3.10 Different ways of partitioning the instance space. 80

Figure 3.11 Different ways of representing clusters. 82

Figure 4.1 Pseudocode for 1R. 86

Figure 4.2 Tree stumps for the weather data. 100

Figure 4.3 Expanded tree stumps for the weather data. 102

Figure 4.4 Decision tree for the weather data. 103

Figure 4.5 Tree stump for the ID code attribute. 105

Figure 4.6 Covering algorithm. 109

Figure 4.7 The instance space during operation of a covering algorithm. 110

Figure 4.8 Pseudocode for a basic rule learner. 114

Figure 4.9 Logistic regression. 127

Figure 4.10 The perceptron. 129

Figure 4.11 The Winnow algorithm. 130

Figure 4.12 A kD-tree for four training instances. 133

Figure 4.13 Using a kD-tree to ﬁnd the nearest neighbor of the star. 134

Figure 4.14 Ball tree for 16 training instances. 136

Figure 4.15 Ruling out an entire ball (gray) based on a target point

(star) and its current nearest neighbor. 137

Figure 4.16 A ball tree. 141

Figure 5.1 A hypothetical lift chart. 170

Figure 5.2 Analyzing the expected beneﬁt of a mailing campaign. 171

Figure 5.3 A sample ROC curve. 173

Figure 5.4 ROC curves for two learning schemes. 174

Figure 5.5 Effect of varying the probability threshold. 178

Figure 6.1 Example of subtree raising. 196

List of Figures

xvi List of Figures

Figure 6.2 Pruning the labor negotiations decision tree. 200

Figure 6.3 Algorithm for forming rules by incremental reduced-error

pruning. 207

Figure 6.4 RIPPER. 209

Figure 6.5 Algorithm for expanding examples into a partial tree. 210

Figure 6.6 Example of building a partial tree. 211

Figure 6.7 Rules with exceptions for the iris data. 213

Figure 6.8 Extended preﬁx trees for the weather data. 220

Figure 6.9 A maximum-margin hyperplane. 225

Figure 6.10 Support vector regression. 228

Figure 6.11 Example datasets and corresponding perceptrons. 233

Figure 6.12 Step versus sigmoid. 240

Figure 6.13 Gradient descent using the error function w

+ 1. 240

Figure 6.14 Multilayer perceptron with a hidden layer. 241

Figure 6.15 Hinge, squared, and 0 – 1 loss functions. 242

Figure 6.16 A boundary between two rectangular classes. 248

Figure 6.17 Pseudocode for model tree induction. 255

Figure 6.18 Model tree for a dataset with nominal attributes. 256

Figure 6.19 A simple Bayesian network for the weather data. 262

Figure 6.20 Another Bayesian network for the weather data. 264

Figure 6.21 The weather data. 270

Figure 6.22 Hierarchical clustering displays. 276

Figure 6.23 Clustering the weather data. 279

Figure 6.24 Hierarchical clusterings of the iris data. 281

Figure 6.25 A two-class mixture model. 285

Figure 6.26 DensiTree showing possible hierarchical clusterings of a given

dataset. 291

Figure 7.1 Attribute space for the weather dataset. 311

Figure 7.2 Discretizing the temperature attribute using the entropy

method. 318

Figure 7.3 The result of discretizing the temperature attribute. 318

Figure 7.4 Class distribution for a two-class, two-attribute problem. 321

Figure 7.5 Principal components transform of a dataset. 325

Figure 7.6 Number of international phone calls from Belgium, 1950–1973. 333

Figure 7.7 Overoptimistic probability estimation for a two-class problem. 344

Figure 8.1 Algorithm for bagging. 355

Figure 8.2 Algorithm for boosting. 359

Figure 8.3 Algorithm for additive logistic regression. 365

Figure 8.4 Simple option tree for the weather data. 366

Figure 8.5 Alternating decision tree for the weather data. 367

Figure 9.1 A tangled “web.” 391

Figure 11.1 The Explorer interface. 408

Figure 11.2 Weather data. 409

Figure 11.3 The Weka Explorer. 410

List of Figures xvii

Figure 11.4 Using J4.8. 411

Figure 11.5 Output from the J4.8 decision tree learner. 412

Figure 11.6 Visualizing the result of J4.8 on the iris dataset. 415

Figure 11.7 Generic Object Editor. 417

Figure 11.8 The SQLViewer tool. 418

Figure 11.9 Choosing a ﬁlter. 420

Figure 11.10 The weather data with two attributes removed. 422

Figure 11.11 Processing the CPU performance data with M5′. 423

Figure 11.12 Output from the M5′ program for numeric prediction. 425

Figure 11.13 Visualizing the errors. 426

Figure 11.14 Working on the segment-challenge data with the User

Classiﬁer. 428

Figure 11.15 Conﬁguring a metalearner for boosting decision stumps. 429

Figure 11.16 Output from the Apriori program for association rules. 430

Figure 11.17 Visualizing the iris dataset. 431

Figure 11.18 Using Weka’s metalearner for discretization. 443

Figure 11.19 Output of NaiveBayes on the weather data. 452

Figure 11.20 Visualizing a Bayesian network for the weather data

(nominal version). 454

Figure 11.21 Changing the parameters for J4.8. 455

Figure 11.22 Output of OneR on the labor negotiations data. 458

Figure 11.23 Output of PART for the labor negotiations data. 460

Figure 11.24 Output of SimpleLinearRegression for the CPU performance

data. 461

Figure 11.25 Output of SMO on the iris data. 463

Figure 11.26 Output of SMO with a nonlinear kernel on the iris data. 465

Figure 11.27 Output of Logistic on the iris data. 468

Figure 11.28 Using Weka’s neural-network graphical user interface. 470

Figure 11.29 Output of SimpleKMeans on the weather data. 481

Figure 11.30 Output of EM on the weather data. 482

Figure 11.31 Clusters formed by DBScan on the iris data. 484

Figure 11.32 OPTICS visualization for the iris data. 485

Figure 11.33 Attribute selection: specifying an evaluator and a search

method. 488

Figure 12.1 The Knowledge Flow interface. 496

Figure 12.2 Conﬁguring a data source. 497

Figure 12.3 Status area after executing the conﬁguration shown in

Figure 12.1. 497

Figure 12.4 Operations on the Knowledge Flow components. 500

Figure 12.5 A Knowledge Flow that operates incrementally. 503

Figure 13.1 An experiment. 506

Figure 13.2 Statistical test results for the experiment in Figure 13.1. 509

Figure 13.3 Setting up an experiment in advanced mode. 511

Figure 13.4 An experiment in clustering. 513

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Terence_Li

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