数据挖掘实战：Weka官方教材第三版

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"《DataMining: Practical Machine Learning Tools and Techniques》是Weka的官方教材，由Ian H. Witten、Eibe Frank和Mark A. Hall合著，属于第三版。该书由Morgan Kaufmann Publishers（Elsevier的一个印记）出版。" 这本书详细介绍了数据挖掘的实践方法以及机器学习工具和技术，是深入理解Weka这一开源数据挖掘软件的权威指南。在数据科学领域，Weka是一个广泛使用的工具，它提供了多种数据预处理、分类、回归、聚类和关联规则学习的算法。书中涵盖的内容可能包括以下几个核心知识点： 1. **数据挖掘基础**：讲解数据挖掘的基本概念，包括数据预处理的重要性，如数据清洗、转换和规范化，以及数据集的划分和评估标准。 2. **机器学习算法**：介绍各种监督和无监督学习算法，如决策树（C4.5, ID3），贝叶斯分类器，支持向量机（SVM），神经网络，K-近邻（KNN），朴素贝叶斯，集成学习（如随机森林），以及聚类算法（如K-Means，层次聚类等）。 3. **特征选择与降维**：探讨如何减少数据维度，提高模型的效率和泛化能力，如主成分分析（PCA）和特征提取方法。 4. **评估与验证**：讨论交叉验证、留出法、自助法等评估模型性能的方法，以及如何理解和解释模型的预测误差。 5. **案例研究与应用**：提供实际案例，展示如何在不同领域如医疗、金融、电子商务等应用数据挖掘技术解决实际问题。 6. **Weka工作流程**：详细介绍如何使用Weka界面进行数据导入、预处理、选择模型、训练和测试，以及如何通过代码接口（如Java API）来编程操作Weka。 7. **最新进展**：第三版可能涵盖了自第二版以来机器学习领域的最新发展，如深度学习、强化学习等前沿技术。 8. **实用技巧与最佳实践**：指导读者如何有效地使用数据挖掘工具，避免常见的陷阱，提高数据分析的效率和结果的可靠性。这本书不仅适合初学者，也适合有一定经验的数据科学家和研究人员，它既包含了理论知识，又提供了丰富的实践指导，是深入了解和使用Weka进行数据挖掘的宝贵资源。

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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