数据挖掘实战：机器学习工具与技术第三版

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"Data Mining 3rd Edition - 一本实践性的机器学习工具和技术的入门教程，适合数据挖掘初学者。由Ian H. Witten, Eibe Frank, Mark A. Hall合著，由Morgan Kaufmann Publishers (Elsevier的一个印记)出版。" 《数据挖掘：实践中的机器学习工具与技术》第三版是一本全面介绍数据挖掘领域的经典著作，针对初次接触该领域的读者设计。这本书深入浅出地讲解了数据挖掘的基本概念、方法和应用，是学习数据挖掘的绝佳教材。作者团队包括Ian H. Witten、Eibe Frank和Mark A. Hall，他们在数据挖掘和机器学习领域有着丰富的研究和教学经验，确保了本书内容的专业性和实用性。书中涵盖了从数据预处理、特征选择、模式发现到结果评估等数据挖掘的整个流程，同时结合了最新的算法和技术发展。本书的核心知识点包括： 1. **数据挖掘基础**：介绍了数据挖掘的概念，包括关联规则、分类、聚类、回归等基本任务，以及它们在实际问题中的应用。 2. **机器学习算法**：详细阐述了决策树、贝叶斯网络、神经网络、支持向量机、随机森林等多种机器学习算法的原理和实现。 3. **数据预处理**：讨论了数据清洗、缺失值处理、异常值检测、数据规范化等数据预处理技术，这些是有效挖掘的前提。 4. **特征工程**：讲述了如何选择和构造有意义的特征，以提高模型的预测能力和解释性。 5. **模式评估**：介绍了准确率、召回率、F1分数、ROC曲线等评估指标，帮助读者理解模型性能并进行选择。 6. **案例研究**：通过真实世界的案例，展示了数据挖掘的实际应用过程，如市场篮子分析、客户细分、信用风险评估等。 7. **工具和技术**：可能涉及WEKA、R、Python等开源工具，以及大数据处理框架如Hadoop和Spark在数据挖掘中的使用。 8. **最新进展**：书中可能会涵盖近十年来数据挖掘领域的最新研究成果，如深度学习、流式数据挖掘和图挖掘等。此外，本书还强调了实践经验的重要性，鼓励读者通过实际操作来加深对理论知识的理解。同时，提供了丰富的练习题和项目建议，帮助读者提升技能和解决问题的能力。《数据挖掘：实践中的机器学习工具与技术》第三版是一本全面而实用的教程，它不仅教授了数据挖掘的基础知识，还关注了领域的最新动态，对于任何希望在这个快速发展的领域中打下坚实基础的初学者来说，都是不可或缺的参考资料。

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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