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Data mining: practical machine learning tools and techniques

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Bibliographische Detailangaben
Beteiligte Personen:	Witten, Ian H. 1947- (VerfasserIn), Frank, Eibe (VerfasserIn), Hall, Mark A. (VerfasserIn), Pal, Christopher J. (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	Amsterdam Morgan Kaufmann [2017]
Ausgabe:	Fourth edition
Schlagwörter:	Data Mining Maschinelles Lernen Java > Programmiersprache Weka 3 Java Data mining
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029406557&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029406557&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
Abstract:	"Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches." -- back of cover
Umfang:	xxxii, 621 Seiten Illustrationen, Diagramme 24 cm
ISBN:	9780128042915

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adam_text	Contents List of Figures........................................................... xv List of Tables.............................................................xxi Preface..................................................................xxiii PART I INTRODUCTION TO DATA MINING CHAPTER t What’s it ail about?............................................3 1.1 Data Mining and Machine Learning...........................4 Describing Structural Patterns.............................6 Machine Learning...........................................7 Data Mining................................................9 1.2 Simple Examples: The Weather Problem and Others...........9 The Weather Problem.......................................10 Contact Lenses: An Idealized Problem......................12 Irises: A Classic Numeric Dataset.........................14 CPU Performance: Introducing Numeric Prediction...........16 Labor Negotiations: A More Realistic Example..............16 Soybean Classification: A Classic Machine Learning Success...............................................19 1.3 Fielded Applications.....................................21 Web Mining.............................................. 21 Decisions Involving Judgment.......................... 22 Screening Images..........................................23 Load Forecasting..........................................24 Diagnosis.................................................25 Marketing and Sales.......................................26 Other Applications........................................27 1.4 The Data Mining Process..................................28 1.5 Machine Learning and Statistics..........................30 1.6 Generalization as Search.................................31 Enumerating the Concept Space.............................32 Bias......................................................33 1.7 Data Mining and Ethics...................................35 Reidentification..........................................36 Using Personal Information................................37 Wider Issues..............................................38 1.8 Further Reading and Bibliographic Notes..................38 CHAPTER 2 Input: concepts, instances, attributes...........................43 2.1 What’s a Concept?...........................................44 2.2 What’s in an Example?......................................46 Relations...................................................47 Other Example Types.........................................51 2.3 What’s in an Attribute?....................................53 2.4 Preparing the Input........................................56 Gathering the Data Together.................................56 ARFF Format.................................................57 Sparse Data.................................................60 Attribute Types.............................................61 Missing Values..............................................62 Inaccurate Values...........................................63 Unbalanced Data.............................................64 Getting to Know Your Data...................................65 2.5 Further Reading and Bibliographic Notes....................65 CHAPTER 3 Output: knowledge representation.................................67 3.1 Tables......................................................68 3.2 Linear Models..............................................68 3.3 Trees.....................................................70 3.4 Rules...,,.................................................75 Classification Rules........................................75 Association Rules...........................................79 Rules With Exceptions.......................................80 More Expressive Rules.......................................82 3.5 Instance-Based Representation..............................84 3.6 Clusters...................................................87 3.7 Further Reading and Bibliographic Notes....................88 CHAPTER 4 Algorithms: the basic methods....................................91 4.1 Inferring Rudimentary Rules................................93 Missing Values and Numeric Attributes.......................94 4.2 Simple Probabilistic Modeling..............................96 Missing Values and Numeric Attributes......................100 Naive Bayes for Document Classification....................103 Remarks....................................................105 4.3 Divide-and-Conquer: Constructing Decision Trees...........105 Calculating Information....................................108 Highly Branching Attributes................................110 4.4 Covering Algorithms: Constructing Rules....................113 Rules Versus Trees......................................114 A Simple Covering Algorithm...............................115 Rules Versus Decision Lists...............................119 4.5 Mining Association Rules...................................120 Item Sets.................................................120 Association Rules.........................................122 Generating Rules Efficiently..............................124 4.6 Linear Models..............................................128 Numeric Prediction: Linear Regression.....................128 Linear Classification: Logistic Regression................129 Linear Classification Using the Perceptron................131 Linear Classification Using Winnow........................133 4.7 Instance-Based Learning....................................135 The Distance Function.....................................135 Finding Nearest Neighbors Efficiently.....................136 Remarks...................................................141 4.8 Clustering.................................................141 Iterative Distance-Based Clustering.......................142 Faster Distance Calculations..............................144 Choosing the Number of Clusters...........................146 Hierarchical Clustering...................................147 Example of Hierarchical Clustering........................148 Incremental Clustering....................................150 Category Utility..........................................154 Remarks...................................................156 4.9 Multi-instance Learning....................................156 Aggregating the Input.....................................157 Aggregating the Output....................................157 4.10 Further Reading and Bibliographic Notes...................158 4.11 WEKA Implementations......................................160 CHAPTER 5 Credibility: evaluating what’s been learned....................161 5.1 Training and Testing..................................... 163 5.2 Predicting Performance.....................................165 5.3 Cross-Validation...........................................167 5.4 Other Estimates............................................169 Leave-One-Out...............................................169 The Bootstrap...............................................169 5.5 Hyperparameter Selection...................................171 5.6 Comparing Data Mining Schemes.............................172 5.7 Predicting Probabilities..................................176 Quadratic Loss Function...................................177 Informational Loss Function...............................178 Remarks...................................................179 5.8 Counting the Cost.........................................179 Cost-Sensitive Classification.............................182 Cost-Sensitive Learning...................................183 Lift Charts...............................................183 ROC Curves................................................186 Recall-Precision Curves...................................190 Remarks...................................................190 Cost Curves...............................................192 5.9 Evaluating Numeric Prediction.............................194 5.10 The MDL Principle........................................197 5.11 Applying the MDL Principle to Clustering.................200 5.12 Using a Validation Set for Model Selection...............201 5.13 Further Reading and Bibliographic Notes..................202 PART II MORE ADVANCED MACHINE LEARNING SCHEMES CHAPTER 6 Trees and rules..............................................209 6.1 Decision Trees.........................................210 Numeric Attributes......................................210 Missing Values..........................................212 Pruning.................................................213 Estimating Error Rates..................................215 Complexity of Decision Tree Induction...................217 From Trees to Rules.....................................219 C4.5: Choices and Options...............................219 Cost-Complexity Pruning.................................220 Discussion..............................................221 6.2 Classification Rules...................................221 Criteria for Choosing Tests.............................222 Missing Values, Numeric Attributes......................223 Generating Good Rules...................................224 Using Global Optimization...............................226 Obtaining Rules From Partial Decision Trees.............227 Rules With Exceptions................................. 231 Discussion..............................................233 6.3 Association Rules.......................................234 Building a Frequent Pattern Tree.........................235 Finding Large Item Sets..................................240 Discussion...............................................241 6.4 WEKA Implementations....................................242 CHAPTER 7 Extending instance-based and linear models....................243 7.1 Instance-Based Learning.................................244 Reducing the Number of Exemplars.........................245 Pruning Noisy Exemplars..................................245 Weighting Attributes.....................................246 Generalizing Exemplars...................................247 Distance Functions for Generalized Exemplars.............248 Generalized Distance Functions...........................250 Discussion...............................................250 7.2 Extending Linear Models.................................252 The Maximum Margin Hyperplane............................253 Nonlinear Class Boundaries...............................254 Support Vector Regression................................256 Kernel Ridge Regression..................................258 The Kernel Perceptron....................................260 Multilayer Perceptrons...................................261 Radial Basis Function Networks...........................270 Stochastic Gradient Descent..............................270 Discussion...............................................272 7.3 Numeric Prediction With Local Linear Models.............273 Model Trees..............................................274 Building the Tree........................................275 Pruning the Tree.........................................275 Nominal Attributes.......................................276 Missing Values...........................................276 Pseudocode for Model Tree Induction......................277 Rules From Model Trees...................................281 Locally Weighted Linear Regression.......................281 Discussion...............................................283 7.4 WEKA Implementations....................................284 CHAPTER 8 Data transformations..........................................285 8.1 Attribute Selection.....................................288 Scheme-Independent Selection.............................289 Searching the Attribute Space............................292 Scheme-Specific Selection................................293 8.2 Discretizing Numeric Attributes............................296 Unsupervised Discretization................................297 Entropy-Based Discretization...............................298 Other Discretization Methods...............................301 Entropy-Based Versus Error-Based Discretization............302 Converting Discrete to Numeric Attributes..................303 8.3 Projections................................................304 Principal Component Analysis...............................305 Random Projections.........................................307 Partial Least Squares Regression...........................307 Independent Component Analysis.............................309 Linear Discriminant Analysis...............................310 Quadratic Discriminant Analysis............................310 Fisher’s Linear Discriminant Analysis......................311 Text to Attribute Vectors..................................313 Time Series................................................314 8.4 Sampling...................................................315 Reservoir Sampling....................................... 315 8.5 Cleansing................................................ 316 Improving Decision Trees...................................316 Robust Regression..........................................317 Detecting Anomalies........................................318 One-Class Learning.........................................319 Outlier Detection..........................................320 Generating Artificial Data.................................321 8.6 Transforming Multiple Classes to Binary Ones...............322 Simple Methods.............................................323 Error-Correcting Output Codes..............................324 Ensembles of Nested Dichotomies............................326 8.7 Calibrating Class Probabilities............................328 8.8 Further Reading and Bibliographic Notes....................331 8.9 WEKA Implementations.......................................334 CHAPTER 9 Probabilistic methods............................................335 9.1 Foundations................................................336 Maximum Likelihood Estimation............................ 338 Maximum a Posteriori Parameter Estimation..................339 9.2 Bayesian Networks..........................................339 Making Predictions.........................................340 Learning Bayesian Networks.................................344 Specific Algorithms........................................347 Data Structures for Fast Learning..........................349 9.3 Clustering and Probability Density Estimation.............352 The Expectation Maximization Algorithm for a Mixture of Gaussians...........................................353 Extending the Mixture Model................................356 Clustering Using Prior Distributions.......................358 Clustering With Correlated Attributes......................359 Kernel Density Estimation..................................361 Comparing Parametric, Semiparametric and Nonparametric Density Models for Classification......................362 9.4 Hidden Variable Models....................................363 Expected Log-Likelihoods and Expected Gradients............364 The Expectation Maximization Algorithm.....................365 Applying the Expectation Maximization Algorithm to Bayesian Networks...................................366 9.5 Bayesian Estimation and Prediction........................367 Probabilistic Inference Methods............................368 9.6 Graphical Models and Factor Graphs........................370 Graphical Models and Plate Notation........................371 Probabilistic Principal Component Analysis.................372 Latent Semantic Analysis...................................376 Using Principal Component Analysis for Dimensionality Reduction..............................................377 Probabilistic LSA..........................................378 Latent Dirichlet Allocation................................379 Factor Graphs............................................ 382 Markov Random Fields.......................................385 Computing Using the Sum-Product and Max-Product Algorithms.............................................386 9.7 Conditional Probability Models............................392 Linear and Polynomial Regression as Probability Models.................................................392 Using Priors on Parameters.................................393 Multiclass Logistic Regression.............................396 Gradient Descent and Second-Order Methods..................400 Generalized Linear Models..................................400 Making Predictions for Ordered Classes.....................402 Conditional Probabilistic Models Using Kernels.............402 9.8 Sequential and Temporal Models..........................403 Markov Models and iV-gram Methods.......................403 Hidden Markov Models....................................404 Conditional Random Fields...............................406 9.9 Further Reading and Bibliographic Notes.................410 Software Packages and Implementations...................414 9.10 WEKA Implementations...................................416 CHAPTER 10 Deep learning......................................... 417 10.1 Deep Feedforward Networks...............................420 The MNIST Evaluation....................................421 Losses and Regularization...............................422 Deep Layered Network Architecture.......................423 Activation Functions....................................424 Backpropagation Revisited...............................426 Computation Graphs and Complex Network Structures......429 Checking Backpropagation Implementations................430 10.2 Training and Evaluating Deep Networks...................431 Early Stopping..........................................431 Validation, Cross-Validation, and Hyperparameter Tuning...432 Mini-Batch-Based Stochastic Gradient Descent............433 Pseudocode for Mini-Batch Based Stochastic Gradient Descent...............................................434 Learning Rates and Schedules............................434 Regularization With Priors on Parameters................435 Dropout.................................................436 Batch Normalization.....................................436 Parameter Initialization................................436 Unsupervised Pretraining................................437 Data Augmentation and Synthetic Transformations.........437 10.3 Convolutional Neural Networks...........................437 The ImageNet Evaluation and Very Deep Convolutional Networks..............................................438 From Image Filtering to Leamable Convolutional Layers..439 Convolutional Layers and Gradients......................443 Pooling and Subsampling Layers and Gradients............444 Implementation..........................................445 10.4 Autoencoders............................................445 Pretraining Deep Autoencoders With RBMs.................448 Denoising Autoencoders and Layerwise Training...........448 Combining Reconstructive and Discriminative Learning...449 10.5 Stochastic Deep Networks..............................449 Boltzmann Machines.....................................449 Restricted Boltzmann Machines..........................451 Contrastive Divergence.................................452 Categorical and Continuous Variables...................452 Deep Boltzmann Machines................................453 Deep Belief Networks...................................455 10.6 Recurrent Neural Networks.............................456 Exploding and Vanishing Gradients......................457 Other Recurrent Network Architectures..................459 10.7 Further Reading and Bibliographic Notes...............461 10.8 Deep Learning Software and Network Implementations...464 Theano.................................................464 Tensor Flow............................................464 Torch..................................................465 Computational Network Toolkit..........................465 Caffe..................................................465 Deepleaming4j..........................................465 Other Packages: Lasagne, Keras, and cuDNN..............465 10.9 WEKA Implementations..................................466 CHAPTER 11 Beyond supervised and unsupervised learning................467 11.1 Semisupervised Learning................................468 Clustering for Classification..........................468 Cotraining.............................................470 EM and Cotraining......................................471 Neural Network Approaches..............................471 11.2 Multi-instance Learning...............................472 Converting to Single-Instance Learning.................472 Upgrading Learning Algorithms..........................475 Dedicated Multi-instance Methods.......................475 11.3 Further Reading and Bibliographic Notes...............477 11.4 WEKA Implementations..................................478 CHAPTER 12 Ensemble learning...........................................479 12.1 Combining Multiple Models..............................480 12.2 Bagging...............................................481 Bias-Variance Decomposition............................482 Bagging With Costs.....................................483 12.3 Randomization.........................................484 Randomization Versus Bagging...........................485 Rotation Forests.......................................486 12.4 Boosting................................................486 AdaBoost................................................487 The Power of Boosting...................................489 12.5 Additive Regression.....................................490 Numeric Prediction......................................491 Additive Logistic Regression............................492 12.6 Interpretable Ensembles.................................493 Option Trees............................................494 Logistic Model Trees....................................496 12.7 Stacking................................................497 12.8 Further Reading and Bibliographic Notes.................499 12.9 WEKA Implementations....................................501 CHAPTER 13 Moving on: applications and beyond......................503 13.1 Applying Machine Learning...............................504 13.2 Learning From Massive Datasets..........................506 13.3 Data Stream Learning....................................509 13.4 Incorporating Domain Knowledge..........................512 13.5 Text Mining.............................................515 Document Classification and Clustering.................516 Information Extraction.................................517 Natural Language Processing............................518 13.6 Web Mining..............................................519 Wrapper Induction......................................519 Page Rank..............................................520 13.7 Images and Speech.......................................522 Images.................................................523 Speech.................................................524 13.8 Adversarial Situations..................................524 13.9 Ubiquitous Data Mining..................................527 13.10 Further Reading and Bibliographic Notes................529 13.11 WEKA Implementations...................................532 Appendix A: Theoretical foundations......................................533 Appendix B: The WEKA workbench...........................................553 References...............................................................573 Index....................................................................601 Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Witten, Frank, Hall, and Pal include the techniques of today as well as methods at the leading edge of contemporary research. Key Features Include: • A thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects • Concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods • Downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks in an easy-to-use interactive interface. • Accompanying open-access online courses that introduce practical application of the material in the book.
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geographic	Java (DE-588)4028527-3 gnd
geographic_facet	Java
id	DE-604.BV043998549
illustrated	Illustrated
indexdate	2024-12-20T17:50:51Z
institution	BVB
isbn	9780128042915
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-029406557
oclc_num	971021791
open_access_boolean
owner	DE-12 DE-29T DE-858 DE-862 DE-BY-FWS DE-523 DE-573 DE-B768 DE-91G DE-BY-TUM DE-91 DE-BY-TUM DE-739 DE-92 DE-898 DE-BY-UBR DE-1046 DE-83 DE-20 DE-355 DE-BY-UBR DE-703
owner_facet	DE-12 DE-29T DE-858 DE-862 DE-BY-FWS DE-523 DE-573 DE-B768 DE-91G DE-BY-TUM DE-91 DE-BY-TUM DE-739 DE-92 DE-898 DE-BY-UBR DE-1046 DE-83 DE-20 DE-355 DE-BY-UBR DE-703
physical	xxxii, 621 Seiten Illustrationen, Diagramme 24 cm
publishDate	2017
publishDateSearch	2017
publishDateSort	2017
publisher	Morgan Kaufmann
record_format	marc
spellingShingle	Witten, Ian H. 1947- Frank, Eibe Hall, Mark A. Pal, Christopher J. Data mining practical machine learning tools and techniques Data Mining (DE-588)4428654-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Java Programmiersprache (DE-588)4401313-9 gnd Weka 3 (DE-588)1126597503 gnd
subject_GND	(DE-588)4428654-5 (DE-588)4193754-5 (DE-588)4401313-9 (DE-588)1126597503 (DE-588)4028527-3
title	Data mining practical machine learning tools and techniques
title_auth	Data mining practical machine learning tools and techniques
title_exact_search	Data mining practical machine learning tools and techniques
title_full	Data mining practical machine learning tools and techniques Ian H. Witten (University of Waikato, Hamilton, New Zealand), Eibe Frank (University of Waikato, Hamilton, New Zealand), Mark A. Hall (University of Waikato, Hamilton, New Zealand), Christopher J. Pal (Polytechnique Montréal, Montreal, Quebec, Canada)
title_fullStr	Data mining practical machine learning tools and techniques Ian H. Witten (University of Waikato, Hamilton, New Zealand), Eibe Frank (University of Waikato, Hamilton, New Zealand), Mark A. Hall (University of Waikato, Hamilton, New Zealand), Christopher J. Pal (Polytechnique Montréal, Montreal, Quebec, Canada)
title_full_unstemmed	Data mining practical machine learning tools and techniques Ian H. Witten (University of Waikato, Hamilton, New Zealand), Eibe Frank (University of Waikato, Hamilton, New Zealand), Mark A. Hall (University of Waikato, Hamilton, New Zealand), Christopher J. Pal (Polytechnique Montréal, Montreal, Quebec, Canada)
title_short	Data mining
title_sort	data mining practical machine learning tools and techniques
title_sub	practical machine learning tools and techniques
topic	Data Mining (DE-588)4428654-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Java Programmiersprache (DE-588)4401313-9 gnd Weka 3 (DE-588)1126597503 gnd
topic_facet	Data Mining Maschinelles Lernen Java Programmiersprache Weka 3 Java
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029406557&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029406557&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT wittenianh dataminingpracticalmachinelearningtoolsandtechniques AT frankeibe dataminingpracticalmachinelearningtoolsandtechniques AT hallmarka dataminingpracticalmachinelearningtoolsandtechniques AT palchristopherj dataminingpracticalmachinelearningtoolsandtechniques

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