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首页Paweł Cichosz解析数据挖掘算法:R语言实践指南
Paweł Cichosz解析数据挖掘算法:R语言实践指南
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《数据挖掘算法》是一本由Paweł Cichosz撰写的专业书籍,针对机器学习算法的各个方面进行了深入探讨。该书是基于R语言进行讲解,适合那些希望深入了解数据挖掘技术并掌握R语言应用的读者。作者Paweł Cichosz隶属于华沙工业大学电子与信息技术系,这表明本书不仅理论性强,而且可能结合了实际的教学经验和案例研究。 书中详细介绍了数据挖掘的基本概念、原理和各种算法,包括但不限于分类、聚类、关联规则学习、回归分析等关键方法。R语言在数据挖掘中的重要性体现在其丰富的库和强大的数据处理能力,使得复杂的数据预处理、模型构建和结果解读变得更加便捷。通过这本书,读者能够学习到如何运用R进行数据清洗、特征选择、模型训练以及性能评估等实践技能。 《数据挖掘算法:解释与应用》这一版于2015年首次出版,由John Wiley & Sons有限公司发行,版权信息完整,强调了作者身份和尊重知识产权的重要性。根据版权法,未经许可,任何形式的复制、存储、传输都必须获得出版社的授权。Wiley的全球编辑办公室、客户服务以及关于版权材料使用的详细信息可以在其官方网站www.wiley.com上找到。 《数据挖掘算法》是一本既理论扎实又实用的指南,对于那些希望在数据分析领域取得进步,特别是对R语言感兴趣的人来说,它提供了宝贵的资源和学习路径。阅读这本书,读者不仅能提升自己的技术能力,还能了解到数据挖掘在现实世界中的应用场景,有助于在信息技术行业中提升竞争力。
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Trim size: 170mm x 244mmCichosz ftoc.tex V2 - 11/04/2014 10:23 A.M. Page xiv
xiv CONTENTS
12.4 Beyond means 338
12.4.1 k-Medians 338
12.4.2 k-Medoids 339
12.5 Beyond (xed) k 342
12.5.1 Multiple runs 343
12.5.2 Adaptive k-centers 343
12.6 Explicit cluster modeling 343
12.7 Conclusion 345
12.8 Further readings 345
References 347
13 Hierarchical clustering 349
13.1 Introduction 349
13.1.1 Basic approaches 349
13.1.2 (Dis)similarity measures 349
13.2 Cluster hierarchies 351
13.2.1 Motivation 351
13.2.2 Model representation 352
13.3 Agglomerative clustering 353
13.3.1 Algorithm scheme 353
13.3.2 Cluster linkage 356
13.4 Divisive clustering 361
13.4.1 Algorithm scheme 361
13.4.2 Wrapping a at clustering algorithm 361
13.4.3 Stop criteria 362
13.5 Hierarchical clustering visualization 364
13.6 Hierarchical clustering prediction 366
13.6.1 Cutting cluster hierarchies 366
13.6.2 Cluster membership assignment 368
13.7 Conclusion 369
13.8 Further readings 370
References 371
14 Clustering model evaluation 373
14.1 Introduction 373
14.1.1 Dataset performance 373
14.1.2 Training performance 374
14.1.3 True performance 374
14.2 Per-cluster quality measures 376
14.2.1 Diameter 376
14.2.2 Separation 377
14.2.3 Isolation 378
14.2.4 Silhouette width 379
14.2.5 Davies–Bouldin index 382
14.3 Overall quality measures 385
14.3.1 Dunn index 386
14.3.2 Average Davies–Bouldin index 387
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Trim size: 170mm x 244mmCichosz ftoc.tex V2 - 11/04/2014 10:23 A.M. Page xv
CONTENTS xv
14.3.3 C index 388
14.3.4 Average silhouette width 389
14.3.5 Loglikelihood 390
14.4 External quality measures 393
14.4.1 Misclassication error 393
14.4.2 Rand index 394
14.4.3 General relationship detection measures 396
14.5 Using quality measures 397
14.6 Conclusion 398
14.7 Further readings 398
References 399
Part V Getting Better Models 401
15 Model ensembles 403
15.1 Introduction 403
15.2 Model committees 404
15.3 Base models 406
15.3.1 Different training sets 406
15.3.2 Different algorithms 412
15.3.3 Different parameter setups 412
15.3.4 Algorithm randomization 412
15.3.5 Base model diversity 418
15.4 Model aggregation 420
15.4.1 Voting/Averaging 420
15.4.2 Probability averaging 422
15.4.3 Weighted voting/averaging 424
15.4.4 Using as attributes 427
15.5 Specic ensemble modeling algorithms 431
15.5.1 Bagging 431
15.5.2 Stacking 433
15.5.3 Boosting 433
15.5.4 Random forest 443
15.5.5 Random Naïve Bayes 446
15.6 Quality of ensemble predictions 448
15.7 Conclusion 449
15.8 Further readings 450
References 451
16 Kernel methods 454
16.1 Introduction 454
16.2 Support vector machines 457
16.2.1 Classication margin 457
16.2.2 Maximum-margin hyperplane 460
16.2.3 Primal form 460
16.2.4 Dual form 464
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Trim size: 170mm x 244mmCichosz ftoc.tex V2 - 11/04/2014 10:23 A.M. Page xvi
xvi CONTENTS
16.2.5 Soft margin 468
16.3 Support vector regression 473
16.3.1 Regression tube 474
16.3.2 Primal form 475
16.3.3 Dual form 475
16.4 Kernel trick 482
16.5 Kernel functions 484
16.5.1 Linear kernel 485
16.5.2 Polynomial kernel 485
16.5.3 Radial kernel 485
16.5.4 Sigmoid kernel 486
16.6 Kernel prediction 487
16.7 Kernel-based algorithms 489
16.7.1 Kernel-based SVM 489
16.7.2 Kernel-based SVR 492
16.8 Conclusion 494
16.9 Further readings 495
References 496
17 Attribute transformation 498
17.1 Introduction 498
17.2 Attribute transformation task 499
17.2.1 Target task 499
17.2.2 Target attribute 500
17.2.3 Transformed attribute 500
17.2.4 Training set 500
17.2.5 Modeling transformations 500
17.2.6 Nonmodeling transformations 503
17.3 Simple transformations 504
17.3.1 Standardization 504
17.3.2 Normalization 505
17.3.3 Aggregation 506
17.3.4 Imputation 507
17.3.5 Binary encoding 508
17.4 Multiclass encoding 510
17.4.1 Encoding and decoding functions 511
17.4.2 1-ok-k encoding 514
17.4.3 Error-correcting encoding 515
17.4.4 Effects of multiclass encoding 519
17.5 Conclusion 521
17.6 Further readings 521
References 522
18 Discretization 524
18.1 Introduction 524
18.2 Discretization task 525
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![](https://csdnimg.cn/release/download_crawler_static/10950619/bg13.jpg)
Trim size: 170mm x 244mmCichosz ftoc.tex V2 - 11/04/2014 10:23 A.M. Page xvii
CONTENTS xvii
18.2.1 Motivation 525
18.2.2 Task denition 526
18.2.3 Discretization as modeling 527
18.2.4 Discretization quality 529
18.3 Unsupervised discretization 530
18.3.1 Equal-width intervals 530
18.3.2 Equal-frequency intervals 531
18.3.3 Nonmodeling discretization 532
18.4 Supervised discretization 533
18.4.1 Pure-class discretization 533
18.4.2 Bottom-up discretization 535
18.4.3 Top-down discretization 546
18.5 Effects of discretization 551
18.6 Conclusion 553
18.7 Further readings 553
References 556
19 Attribute selection 558
19.1 Introduction 558
19.2 Attribute selection task 559
19.2.1 Motivation 559
19.2.2 Task denition 560
19.2.3 Algorithms 561
19.3 Attribute subset search 562
19.3.1 Search task 562
19.3.2 Initial state 563
19.3.3 Search operators 564
19.3.4 State selection 564
19.3.5 Stop criteria 565
19.4 Attribute selection lters 568
19.4.1 Simple statistical lters 568
19.4.2 Correlation-based lters 571
19.4.3 Consistency-based lters 575
19.4.4 RELIEF 577
19.4.5 Random forest 584
19.4.6 Cutoff criteria 585
19.4.7 Filter-driven search 586
19.5 Attribute selection wrappers 588
19.5.1 Subset evaluation 588
19.5.2 Wrapper attribute selection 591
19.6 Effects of attribute selection 593
19.7 Conclusion 598
19.8 Further readings 599
References 600
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Trim size: 170mm x 244mmCichosz ftoc.tex V2 - 11/04/2014 10:23 A.M. Page xviii
xviii CONTENTS
20 Case studies 602
20.1 Introduction 602
20.1.1 Datasets 603
20.1.2 Packages 603
20.1.3 Auxiliary functions 603
20.2 Census income 605
20.2.1 Data loading and preprocessing 606
20.2.2 Default model 608
20.2.3 Incorporating misclassication costs 610
20.2.4 Pruning 616
20.2.5 Attribute selection 624
20.2.6 Final models 628
20.3 Communities and crime 631
20.3.1 Data loading 632
20.3.2 Data quality 632
20.3.3 Regression trees 634
20.3.4 Linear models 635
20.3.5 Attribute selection 636
20.3.6 Piecewise-linear models 639
20.4 Cover type 640
20.4.1 Data loading and preprocessing 640
20.4.2 Class imbalance 641
20.4.3 Decision trees 641
20.4.4 Class rebalancing 644
20.4.5 Multiclass encoding 647
20.4.6 Final classication models 649
20.4.7 Clustering 650
20.5 Conclusion 654
20.6 Further readings 655
References 655
Closing 657
A Notation 659
A.1 Attribute values 659
A.2 Data subsets 659
A.3 Probabilities 660
B R packages 661
B.1 CRAN packages 661
B.2 DMR packages 662
B.3 Installing packages 663
References 664
C Datasets 666
Index 667
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