Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
Moving on: Extensions and applications
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- Bu səhifədəki naviqasiya:
- Adversarial situations 356 8.5 Ubiquitous data mining 358 8.6 Further reading
- Incremental learning 433 12 The Experimenter 437 12.1 Getting started
- Going through the code 462
8 Moving on: Extensions and applications 345 8.1 Learning from massive datasets 346 8.2 Incorporating domain knowledge 349 8.3 Text and Web mining 351 8.4 Adversarial situations 356 8.5 Ubiquitous data mining 358 8.6 Further reading 361 Part II The Weka machine learning workbench 363 9 Introduction to Weka 365 9.1 What’s in Weka? 366 9.2 How do you use it? 367 9.3 What else can you do? 368 9.4 How do you get it? 368 10 The Explorer 369 10.1 Getting started 369 Preparing the data 370 Loading the data into the Explorer 370 Building a decision tree 373 Examining the output 373 Doing it again 377 Working with models 377 When things go wrong 378 10.2 Exploring the Explorer 380 Loading and filtering files 380 Training and testing learning schemes 384 Do it yourself: The User Classifier 388 Using a metalearner 389 Clustering and association rules 391 Attribute selection 392 Visualization 393 10.3 Filtering algorithms 393 Unsupervised attribute filters 395 Unsupervised instance filters 400 Supervised filters 401 C O N T E N TS x i i i P088407-FM.qxd 4/30/05 10:55 AM Page xiii 10.4 Learning algorithms 403 Bayesian classifiers 403 Trees 406 Rules 408 Functions 409 Lazy classifiers 413 Miscellaneous classifiers 414 10.5 Metalearning algorithms 414 Bagging and randomization 414 Boosting 416 Combining classifiers 417 Cost-sensitive learning 417 Optimizing performance 417 Retargeting classifiers for different tasks 418 10.6 Clustering algorithms 418 10.7 Association-rule learners 419 10.8 Attribute selection 420 Attribute subset evaluators 422 Single-attribute evaluators 422 Search methods 423 11 The Knowledge Flow interface 427 11.1 Getting started 427 11.2 The Knowledge Flow components 430 11.3 Configuring and connecting the components 431 11.4 Incremental learning 433 12 The Experimenter 437 12.1 Getting started 438 Running an experiment 439 Analyzing the results 440 12.2 Simple setup 441 12.3 Advanced setup 442 12.4 The Analyze panel 443 12.5 Distributing processing over several machines 445 x i v
C O N T E N TS P088407-FM.qxd 4/30/05 10:55 AM Page xiv 13 The command-line interface 449 13.1 Getting started 449 13.2 The structure of Weka 450 Classes, instances, and packages 450 The weka.core package 451 The weka.classifiers package 453 Other packages 455 Javadoc indices 456 13.3 Command-line options 456 Generic options 456 Scheme-specific options 458 14 Embedded machine learning 461 14.1 A simple data mining application 461 14.2 Going through the code 462 main() 462 MessageClassifier() 462 updateData() 468 classifyMessage() 468 15 Writing new learning schemes 471 15.1 An example classifier 471 buildClassifier() 472 makeTree() 472 computeInfoGain() 480 classifyInstance() 480 main() 481 15.2 Conventions for implementing classifiers 483 References 485 Index 505 About the authors 525 C O N T E N TS x v P088407-FM.qxd 5/3/05 9:13 AM Page xv Yüklə 4,3 Mb. Dostları ilə paylaş:
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