数据挖掘：机器学习实用工具与技术第三版

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数据挖掘(Data Mining)是机器学习(Practical Machine Learning)中的核心领域，它涉及从大量数据中提取有价值的信息和知识，以支持决策制定、预测分析和模式识别。第三版的《数据挖掘：实用机器学习工具与技术》由伊恩·H·威滕(Ian H. Witten)，埃比·弗兰克(Eibe Frank)和马克·A·霍尔(Mark A. Hall)合著，由摩根凯夫曼出版社(Morgan Kaufmann Publishers)出版，隶属于Elsevier集团。该书版权归属Elsevier，2011年首次发行，强调了在未经许可的情况下，任何形式的复制或传播都是禁止的，包括电子和机械手段，如影印、录音或信息存储和检索系统。本书旨在提供实践者所需的深入理解，涵盖数据挖掘的基本原理、技术和算法，包括但不限于分类、聚类、关联规则挖掘、序列模式挖掘以及深度学习方法。读者将学习如何运用这些工具和技术处理和解析大数据，发现隐藏的规律和趋势，以提升业务效率和竞争力。书中内容包括但不限于以下部分： 1. 数据预处理：介绍如何清洗、整理和转换原始数据，使其适合于挖掘过程。 2. 概念描述：对数据挖掘的概念进行清晰阐述，解释其在决策支持系统、商业智能和人工智能中的角色。 3. 分类算法：探讨各种分类算法，如决策树、朴素贝叶斯、支持向量机等，以及它们在实际场景中的应用。 4. 聚类分析：讲解如何通过相似性度量和聚类算法将数据分组，揭示数据内在结构。 5. 关联规则学习：讨论频繁模式挖掘和关联规则的发现，用于市场篮子分析和推荐系统。 6. 序列模式挖掘：关注时间序列数据中的重复模式，如序列模式挖掘和序列标注。 7. 深度学习应用：介绍如何将深度学习模型应用于数据挖掘，尤其是在大数据和复杂问题中的表现。 8. 实战案例研究：通过具体的实例展示数据挖掘在不同领域的应用，如金融、医疗、电子商务等。这本数据挖掘专著不仅是一本理论教材，也是一本实操指南，帮助读者掌握数据挖掘的最新工具和技术，从而在当今大数据时代中取得竞争优势。

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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