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Data mining: practical machine learning tools and techniques

Contents: Part I. Machine Learning Tools and Techniques: 1. What's iIt all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what's been learned -- Part II. Advanced Data Mining: 6. Imple...

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Main Authors:	Witten, Ian H. 1947- (Author), Frank, Eibe (Author), Hall, Mark A. (Author)
Format:	Book
Language:	English
Published:	Amsterdam [u.a.] Elsevier 2011
Edition:	3. ed.
Series:	The Morgan Kaufmann series in data management systems
Subjects:	Data Mining Weka 3 Maschinelles Lernen Java > Programmiersprache Java
Online Access:	Ausführliche Beschreibung Inhaltsverzeichnis
Summary:	Contents: Part I. Machine Learning Tools and Techniques: 1. What's iIt all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what's been learned -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. Tutorial exercises for the weka explorer.
Item Description:	Hier auch später erschienene, unveränderte Nachdrucke
Physical Description:	XXXIII, 629 S. Ill., graph. Darst.
ISBN:	9780123748560 0123748569

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adam_text	Contents LIST OF FIGURES .................................................................................................xv LIST OF TABLES ..................................................................................................xix PREFACE ...............................................................................................................xxi Updated and Revised Content ...........................................................................xxv Second Edition ...............................................................................................xxv Third Edition .................................................................................................xxvi ACKNOWLEDGMENTS ....................................................................................xxix ABOUT THE AUTHORS .....................................................:............................xxxiii PART I INTRODUCTION TO DATA MINING _______________ CHAPTER 1 What s It All About? ................................................................3 1.1 Data Mining and Machine Learning ..............................................3 Describing Structural Patterns ........................................................5 Machine Learning ...........................................................................7 Data Mining ....................................................................................8 1.2 Simple Examples: The Weather Problem and Others ....................9 The Weather Problem .....................................................................9 Contact Lenses: An Idealized Problem ........................................12 Irises: A Classic Numeric Dataset ................................................13 CPU Performance: Introducing Numeric Prediction ....................15 Labor Negotiations: A More Realistic Example ..........................15 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 Machine Learning and Statistics ..................................................28 1.5 Generalization as Search .............................................................29 1.6 Data Mining and Ethics ................................................................33 Reidentification .............................................................................33 Using Personal Information ..........................................................34 Wider Issues ..................................................................................35 1.7 Further Reading ............................................................................36 VI Contents CHAPTER 2 Input: Concepts, Instances, and Attributes .............................39 2.1 What s a Concept? ........................................................................40 2.2 What s in an Example? .................................................................42 Relations ........................................................................................43 Other Example Types ....................................................................46 2.3 What s in an Attribute? ...................................„............................49 2.4 Preparing the Input .......................................................................51 Gathering the Data Together .........................................................51 ARFF Format ................................................................................52 Sparse Data ...................................................................................56 Attribute Types ............................_................................................56 Missing Values ..............................................................................58 Inaccurate Values ..........................................................................59 Getting to Know Your Data ..........................................................60 2.5 Further Reading ............................................................................60 CHAPTER 3 Output: Knowledge Representation ........................................61 3.1 Tables ............................................................................................61 3.2 Linear Models ...............................................................................62 3.3 Trees ..............................................................................................64 3.4 Rules ..............................................................................................67 Classification Rules .......................................................................69 Association Rules ..........................................................................72 Rules with Exceptions ..................................................................73 More Expressive Rules .................................................................75 3.5 Instance-Based Representation .....................................................78 3.6 Clusters ..........................................................................................81 3.7 Further Reading ............................................................................83 CHAPTER 4 Algorithms: The Basic Methods .............................................85 4.1 Inferring Rudimentary Rules ........................................................86 Missing Values and Numeric Attributes .......................................87 Discussion .....................................................................................89 4.2 Statistical Modeling ......................................................................90 Missing Values and Numeric Attributes ......................................94 Naïve Bayes for Document Classification ....................................97 Discussion .....................................................................................99 4.3 Divide-and-Conquer: Constructing Decision Trees .....................99 Calculating Information ..............................................................103 Highly Branching Attributes .......................................................105 Discussion ...................................................................................107 Contents vii 4.4 Covering Algorithms: Constructing Rules .................................108 Rules versus Trees ......................................................................109 A Simple Covering Algorithm ....................................................110 Rules versus Decision Lists ........................................................115 4.5 Mining Association Rules ...........................................................116 Item Sets ......................................................................................116 Association Rules ........................................................................119 Generating Rules Efficiently .......................................................122 Discussion ...................................................................................123 4.6 Linear Models .............................................................................124 Numeric Prediction: Linear Regression .....................................124 Linear Classification: Logistic Regression .................................125 Linear Classification Using the Perceptron ................................127 Linear Classification Using Winnow ..........................................129 4.7 Instance-Based Learning .............................................................131 Distance Function .......................................................................131 Finding Nearest Neighbors Efficiently .......................................132 Discussion ...................................................................................137 4.8 Clustering ....................................................................................138 Iterative Distance-Based Clustering ...........................................139 Faster Distance Calculations .......................................................139 Discussion ...................................................................................141 4.9 Multi-Instance Learning ..............................................................141 Aggregating the Input .................................................................142 Aggregating the Output ..............................................................142 Discussion ............:......................................................................142 4.10 Further Reading ..........................................................................143 4.11 Weka Implementations ................................................................145 CHAPTER 5 Credibility: Evaluating What s Been Learned ........................147 5.1 Training and Testing ...................................................................148 5.2 Predicting Performance ...............................................................150 5.3 Cross-Validation ..........................................................................152 5.4 Other Estimates ...........................................................................154 Leave-One-Out Cross-Validation ................................................154 The Bootstrap ..............................................................................155 5.5 Comparing Data Mining Schemes ..............................................156 5.6 Predicting Probabilities ...............................................................159 Quadratic Loss Function .............................................................160 Informational Loss Function .......................................................161 Discussion ...................................................................................162 viii Contents 5.7 Counting the Cost .......................................................................163 Cost-Sensitive Classification ......................................................166 Cost-Sensitive Learning ..............................................................167 Lift Charts ...................................................................................168 ROC Curves ................................................................................172 Recall-Precision Curves .............................................................174 Discussion ...................................................................................175 Cost Curves ................................................................................177 5.8 Evaluating Numeric Prediction ...................................................180 5.9 Minimum Description Length Principle .....................................183 5.10 Applying the MDL Principle to Clustering ................................186 5.11 Further Reading ..........................................................................187 PART И ADVANCED DATA MINING _____________________ CHAPTER 6 Implementations: Real Machine Learning Schemes ..............191 6.1 Decision Trees .............................................................................192 Numeric Attributes ......................................................................193 Missing Values ............................................................................194 Pruning ........................................................................................195 Estimating Error Rates ................................................................197 Complexity of Decision Tree Induction .....................................199 From Trees to Rules ....................................................................200 C4.5: Choices and Options .........................................................201 Cost-Complexity Pruning ...........................................................202 Discussion ...................................................................................202 6.2 Classification Rules .....................................................................203 Criteria for Choosing Tests .........................................................203 Missing Values, Numeric Attributes ...........................................204 Generating Good Rules ...............................................................205 Using Global Optimization .........................................................208 Obtaining Rules from Partial Decision Trees .............................208 Rules with Exceptions ................................................................212 Discussion ...................................................................................215 6.3 Association Rules ........................................................................216 Building a Frequent-Pattern Tree ...............................................216 Finding Large Item Sets .............................................................219 Discussion ...................................................................................222 6.4 Extending Linear Models ...........................................................223 Maximum-Margin Hyperplane...................................................224 Nonlinear Class Boundaries .......................................................226 Contents ix Support Vector Regression..........................................................227 Kernel Ridge Regression............................................................229 Kernel Perceptron.......................................................................231 Multilayer Perceptrons ................................................................232 Radial Basis Function Networks................................................241 Stochastic Gradient Descent .......................................................242 Discussion................................................................................... 243 6.5 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 .................................................249 Discussion ...................................................................................250 6.6 Numeric Prediction with Local Linear Models ..........................251 Model Trees ................................................................................252 Building the Tree ........................................................................253 Pruning the Tree ..........................................................................253 Nominal Attributes ......................................................................254 Missing Values ............................................................................254 Pseudocode for Model Tree Induction .......................................255 Rules from Model Trees .............................................................259 Locally Weighted Linear Regression ..........................................259 Discussion ...................................................................................261 6.7 Bayesian Networks .....................................................................261 Making Predictions .....................................................................262 Learning Bayesian Networks ......................................................266 Specific Algorithms .....................................................................268 Data Structures for Fast Learning ..............................................270 Discussion ...................................................................................273 6.8 Clustering ....................................................................................273 Choosing the Number of Clusters ..............................................274 Hierarchical Clustering ...............................................................274 Example of Hierarchical Clustering ...........................................276 Incremental Clustering ................................................................279 Category Utility ..........................................................................284 Probability-Based Clustering ......................................................285 The EM Algorithm ......................................................................287 Extending the Mixture Model ....................................................289 Contents Bayesian Clustering.................................................................... 290 Discussion ...................................................................................292 6.9 Semisupervised Learning ............................................................294 Clustering for Classification .......................................................294 Co-training ..................................................................................296 EM and Co-training ....................................................................297 Discussion ...................................................................................297 6.10 Multi-Instance Learning ..............................................................298 Converting to Single-Instance Learning .....................................298 Upgrading Learning Algorithms .................................................300 Dedicated Multi-Instance Methods .............................................301 Discussion .......................................................................>...........302 6.11 Weka Implementations ................................................................303 CHAPTER 7 Data Transformations ..........................................................305 7.1 Attribute Selection ......................................................................307 Scheme-Independent Selection ...................................................308 Searching the Attribute Space ....................................................311 Scheme-Specific Selection ..........................................................312 7.2 Discretizing Numeric Attributes .................................................314 Unsupervised Discretization .......................................................316 Entropy-Based Discretization .....................................................316 Other Discretization Methods .....................................................320 Entropy-Based versus Error-Based Discretization .....................320 Converting Discrete Attributes to Numeric Attributes ...............322 7.3 Projections ...................................................................................322 Principal Components Analysis ..................................................324 Random Projections ....................................................................326 Partial Least-Squares Regression ...............................................326 Text to Attribute Vectors .............................................................328 Time Series .................................................................................330 7.4 Sampling .....................................................................................330 Reservoir Sampling .....................................................................330 7.5 Cleansing .....................................................................................331 Improving Decision Trees ...........................................................332 Robust Regression ......................................................................333 Detecting Anomalies ...................................................................334 One-Class Learning ....................................................................335 7.6 Transforming Multiple Classes to Binary Ones .........................338 Simple Methods ..........................................................................338 Error-Correcting Output Codes ..................................................339 Ensembles of Nested Dichotomies .............................................341 Contents XI 7.7 Calibrating Class Probabilities ...................................................343 7.8 Further Reading ..........................................................................346 7.9 Weka Implementations ................................................................348 CHAPTER 8 Ensemble Learning .............................................................351 8.1 Combining Multiple Models .......................................................351 8.2/ Bagging .......................................................................................352 Bias-Variance Decomposition ....................................................353 Bagging with Costs .....................................................................355 8.3 Randomization ............................................................................356 Randomization versus Bagging ..................................................357 Rotation Forests ..........................................................................357 8.4 Boosting ......................................................................................358 AdaBoost .....................................................................................358 The Power of Boosting ...............................................................361 8.5 Additive Regression ....................................................................362 Numeric Prediction .....................................................................362 Additive Logistic Regression .....................................................364 8.6 Interpretable Ensembles ..............................................................365 Option Trees ................................................................................365 Logistic Model Trees ..................................................................368 8.7 Stacking .......................................................................................369 8.8 Further Reading ..........................................................................371 8.9 Weka Implementations ................................................................372 Chapter 9 Moving on: Applications and Beyond ...................................375 9.1 Applying Data Mining ................................................................375 9.2 Learning from Massive Datasets ................................................378 9.3 Data Stream Learning .................................................................380 9.4 Incorporating Domain Knowledge .............................................384 9.5 Text Mining .................................................................................386 9.6 Web Mining .................................................................................389 9.7 Adversarial Situations .................................................................393 9.8 Ubiquitous Data Mining .............................................................395 9.9 Further Reading ..........................................................................397 PART III THE WEKA DATA MINING WORKBENCH ________ CHAPTER 10 Introduction to Weka ..........................................................403 10.1 What s in Weka? .........................................................................403 10.2 How Do You Use It? ..................................................................404 10.3 What Else Can You Do? .............................................................405 10.4 How Do You Get It? ...................................................................406 xii Contents CHAPTER 11 The Explorer .......................................................................407 11.1 Getting Started ............................................................................407 Preparing the Data ......................................................................407 Loading the Data into the Explorer ............................................408 Building a Decision Tree ............................................................410 Examining the Output .................................................................411 Doing It Again ............................................................................413 Working with Models .................................................................414 When Things Go Wrong .............................................................415 11.2 Exploring the Explorer ...............................................................416 Loading and Filtering Files ........................................................416 Training and Testing Learning Schemes ....................................422 Do It Yourself: The User Classifier ............................................424 Using a Metalearner ....................................................................427 Clustering and Association Rules ...............................................429 Attribute Selection ......................................................................430 Visualization ................................................................................430 11.3 Filtering Algorithms ....................................................................432 Unsupervised Attribute Filters ....................................................432 Unsupervised Instance Filters .....................................................441 Supervised Filters ........................................................................443 11.4 Learning Algorithms ...................................................................445 Bayesian Classifiers ....................................................................451 Trees ............................................................................................454 Rules ............................................................................................457 Functions .....................................................................................459 Neural Networks .........................................................................469 Lazy Classifiers ...........................................................................472 Multi-Instance Classifiers ...........................................................472 Miscellaneous Classifiers ............................................................474 11.5 Metalearning Algorithms ............................................................474 Bagging and Randomization .......................................................474 Boosting ......................................................................................476 Combining Classifiers .................................................................477 Cost-Sensitive Learning ..............................................................477 Optimizing Performance .............................................................478 Retargeting Classifiers for Different Tasks ................................479 11.6 Clustering Algorithms .................................................................480 11.7 Association-Rule Learners ..........................................................485 11.8 Attribute Selection ......................................................................487 Attribute Subset Evaluators ........................................................488 Contents xiii Single-Attribute Evaluators........................................................490 Search Methods........................................................................... 492 CHAPTER 12 The Knowledge Flow Interface ............................................495 12.1 Getting Started ............................................................................495 12.2 Components .................................................................................498 12.3 Configuring and Connecting the Components ...........................500 12.4 Incremental Learning ..................................................................502 CHAPTER 13 The Experimenter ...............................................................505 13.1 Getting Started ............................................................................505 Running an Experiment ..............................................................506 Analyzing the Results .................................................................509 13.2 Simple Setup ...............................................................................510 13.3 Advanced Setup ..........................................................................511 13.4 The Analyze Panel ......................................................................512 13.5 Distributing Processing over Several Machines .........................515 CHAPTER 14 The Command-Line Interface ...............................................519 14.1 Getting Started ............................................................................519 14.2 The Structure of Weka ................................................................519 Classes, Instances, and Packages ................................................520 The weka.core Package ...............................................................520 The weka.classifiers Package ......................................................523 Other Packages ............................................................................525 Javadoc Indexes ..........................................................................525 14.3 Command-Line Options ..............................................................526 Generic Options ..........................................................................526 Scheme-Specific Options ............................................................529 CHAPTER 15 Embedded Machine Learning ..............................................531 15.1 A Simple Data Mining Application ............................................531 MessageClassifierQ .....................................................................536 updateDataQ ...............................................................................536 classifyMessageQ ........................................................................537 CHAPTER 16 Writing New Learning Schemes ..........................................539 16.1 An Example Classifier ................................................................539 buildClassifierQ ...........................................................................540 makeTreeQ ...................................................................................540 computelnfoGainQ ......................................................................549 classify Instanced .........................................................................549 xiv Contents toSourceQ....................................................................................550 main()..........................................................................................553 16.2 Conventions for Implementing Classifiers .................................555 Capabilities ..................................................................................555 CHAPTER 17 Tutorial Exercises for the Weka Explorer .............................559 17.1 Introduction to the Explorer Interface ........................................559 Loading a Dataset .......................................................................559 The Dataset Editor ......................................................................560 Applying a Filter .........................................................................561 The Visualize Panel ....................................................................562 The Classify Panel ......................................................................562 17.2 Nearest-Neighbor Learning and Decision Trees ........................566 The Glass Dataset .......................................................................566 Attribute Selection ......................................................................567 Class Noise and Nearest-Neighbor Learning .............................568 Varying the Amount of Training Data ........................................569 Interactive Decision Tree Construction ......................................569 17.3 Classification Boundaries ............................................................571 Visualizing IR .............................................................................571 Visualizing Nearest-Neighbor Learning .....................................572 Visualizing Naïve Bayes .............................................................573 Visualizing Decision Trees and Rule Sets ..................................573 Messing with the Data ................................................................574 17.4 Preprocessing and Parameter Tuning .........................................574 Discretization ..............................................................................574 More on Discretization ...............................................................575 Automatic Attribute Selection ....................................................575 More on Automatic Attribute Selection .....................................576 Automatic Parameter Tuning ......................................................577 17.5 Document Classification .............................................................578 Data with String Attributes .........................................................579 Classifying Actual Documents ...................................................580 Exploring the StringToWordVector Filter ...................................581 17.6 Mining Association Rules ...........................................................582 Association-Rule Mining ............................................................582 Mining a Real-World Dataset .....................................................584 Market Basket Analysis ..............................................................584 REFERENCES ...............................................................................................587 INDEX .........................................................................................................607
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Witten ; Eibe Frank ; Mark A. Hall</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">3. ed.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Amsterdam [u.a.]</subfield><subfield code="b">Elsevier</subfield><subfield code="c">2011</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XXXIII, 629 S.</subfield><subfield code="b">Ill., graph. 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Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. 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geographic	Java (DE-588)4028527-3 gnd
geographic_facet	Java
id	DE-604.BV036896475
illustrated	Illustrated
indexdate	2025-02-20T06:38:23Z
institution	BVB
isbn	9780123748560 0123748569
language	English
lccn	2010039827
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-020811561
oclc_num	706859282
open_access_boolean
owner	DE-20 DE-473 DE-BY-UBG DE-706 DE-11 DE-1049 DE-188 DE-703 DE-573 DE-19 DE-BY-UBM DE-29T DE-91G DE-BY-TUM DE-521 DE-860 DE-634 DE-91 DE-BY-TUM DE-355 DE-BY-UBR DE-523 DE-384 DE-824 DE-945 DE-859 DE-83 DE-1102 DE-M347 DE-862 DE-BY-FWS
owner_facet	DE-20 DE-473 DE-BY-UBG DE-706 DE-11 DE-1049 DE-188 DE-703 DE-573 DE-19 DE-BY-UBM DE-29T DE-91G DE-BY-TUM DE-521 DE-860 DE-634 DE-91 DE-BY-TUM DE-355 DE-BY-UBR DE-523 DE-384 DE-824 DE-945 DE-859 DE-83 DE-1102 DE-M347 DE-862 DE-BY-FWS
physical	XXXIII, 629 S. Ill., graph. Darst.
publishDate	2011
publishDateSearch	2011
publishDateSort	2011
publisher	Elsevier
record_format	marc
series2	The Morgan Kaufmann series in data management systems
spellingShingle	Witten, Ian H. 1947- Frank, Eibe Hall, Mark A. Data mining practical machine learning tools and techniques Data Mining Data Mining (DE-588)4428654-5 gnd Weka 3 (DE-588)1126597503 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Java Programmiersprache (DE-588)4401313-9 gnd
subject_GND	(DE-588)4428654-5 (DE-588)1126597503 (DE-588)4193754-5 (DE-588)4401313-9 (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 ; Eibe Frank ; Mark A. Hall
title_fullStr	Data mining practical machine learning tools and techniques Ian H. Witten ; Eibe Frank ; Mark A. Hall
title_full_unstemmed	Data mining practical machine learning tools and techniques Ian H. Witten ; Eibe Frank ; Mark A. Hall
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 Data Mining (DE-588)4428654-5 gnd Weka 3 (DE-588)1126597503 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Java Programmiersprache (DE-588)4401313-9 gnd
topic_facet	Data Mining Weka 3 Maschinelles Lernen Java Programmiersprache Java
url	http://www.elsevier.com/books/data-mining-practical-machine-learning-tools-and-techniques/witten/978-0-12-374856-0 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020811561&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT wittenianh dataminingpracticalmachinelearningtoolsandtechniques AT frankeibe dataminingpracticalmachinelearningtoolsandtechniques AT hallmarka dataminingpracticalmachinelearningtoolsandtechniques

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