Data Mining: Practical Machine Learning Tools and ... - LIDeCC

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CONTENTSxv13 The command-line interface 44913.1 Getting started 44913.2 The structure of Weka 450Classes, instances, and packages 450The weka.core package 451The weka.classifiers package 453Other packages 455Javadoc indices 45613.3 Command-line options 456Generic options 456Scheme-specific options 45814 Embedded machine learning 46114.1 A simple data mining application 46114.2 Going through the code 462main() 462MessageClassifier() 462updateData() 468classifyMessage() 46815 Writing new learning schemes 47115.1 An example classifier 471buildClassifier() 472makeTree() 472computeInfoGain() 480classifyInstance() 480main() 48115.2 Conventions for implementing classifiers 483References 485Index 505About the authors 525
Page 2: Data MiningPractical Machine Learni
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Page 7 and 8: viFOREWORDThis book presents this n
Page 10 and 11: CONTENTSix4 Algorithms: The basic m
Page 12 and 13: CONTENTSxiGenerating good rules 202
Page 14 and 15: CONTENTSxiii8 Moving on: Extensions
Page 18 and 19: List of FiguresFigure 1.1 Rules for
Page 20 and 21: LIST OF FIGURESxixFigure 6.18 Hiera
Page 22 and 23: List of TablesTable 1.1 The contact
Page 24 and 25: PrefaceThe convergence of computing
Page 26 and 27: PREFACExxvThe book spans the gulf b
Page 28 and 29: PREFACExxviiobject-oriented languag
Page 30 and 31: PREFACExxixare used to find nearest
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1.5 GENERALIZATION AS SEARCH 33If o
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1.6 DATA MINING AND ETHICS 35descri
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1.7 FURTHER READING 37purchases? Sh
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1.7 FURTHER READING 39ing. An excel
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42 CHAPTER 2 | INPUT: CONCEPTS, INS
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chapter 6Implementations:Real Machi
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6.1 DECISION TREES 189Because of th
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6.1 DECISION TREES 191whereas succe
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6.1 DECISION TREES 193which account
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The mathematics involved is just th
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ing that most of the instances are
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6.1 DECISION TREES 199The more rece
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6.2 CLASSIFICATION RULES 201number
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6.2 CLASSIFICATION RULES 203perform
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6.2 CLASSIFICATION RULES 205Initial
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6.2 CLASSIFICATION RULES 209111(a)2
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6.2 CLASSIFICATION RULES 211It is u
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6.2 CLASSIFICATION RULES 213Of the
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6.3 EXTENDING LINEAR MODELS 215cons
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6.3 EXTENDING LINEAR MODELS 217in t
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6.3 EXTENDING LINEAR MODELS 219way
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6.3 EXTENDING LINEAR MODELS 2211086
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6.3 EXTENDING LINEAR MODELS 223This
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11110000(a)0 1(b)0 1(c)0 1(d)C-1.51
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6.3 EXTENDING LINEAR MODELS 227Back
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6.3 EXTENDING LINEAR MODELS 229Grad
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6.3 EXTENDING LINEAR MODELS 231f(x)
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6.3 EXTENDING LINEAR MODELS 233mini
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6.4 INSTANCE-BASED LEARNING 235Disc
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6.4 INSTANCE-BASED LEARNING 237the
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6.4 INSTANCE-BASED LEARNING 239is n
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6.4 INSTANCE-BASED LEARNING 241thir
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6.5 NUMERIC PREDICTION 243eralizati
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6.5 NUMERIC PREDICTION 245Building
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6.5 NUMERIC PREDICTION 247where m i
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6.5 NUMERIC PREDICTION 249by split,
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6.5 NUMERIC PREDICTION 251in Sectio
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6.5 NUMERIC PREDICTION 253nique. It
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6.6 CLUSTERING 255independently. On
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6.6 CLUSTERING 257the first five in
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VirginicaSetosa Setosa Setosa Setos
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6.6 CLUSTERING 261ing the values of
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6.6 CLUSTERING 263distribution give
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6.6 CLUSTERING 265ization process m
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6.6 CLUSTERING 267When the dataset
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6.6 CLUSTERING 269mixture model, pr
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6.7 BAYESIAN NETWORKS 271the X-mean
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6.7 BAYESIAN NETWORKS 273playyes.63
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6.7 BAYESIAN NETWORKS 275For exampl
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6.7 BAYESIAN NETWORKS 277of the thi
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6.7 BAYESIAN NETWORKS 279values for
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6.7 BAYESIAN NETWORKS 281(a)humidit
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6.7 BAYESIAN NETWORKS 283ers to the
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286 CHAPTER 7 | TRANSFORMATIONS: EN
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chapter 8Moving on:Extensions and A
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8.1 LEARNING FROM MASSIVE DATASETS
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8.2 INCORPORATING DOMAIN KNOWLEDGE
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8.3 TEXT AND WEB MINING 351fact pre
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8.3 TEXT AND WEB MINING 353frequent
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8.3 TEXT AND WEB MINING 355markup i
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8.4 ADVERSARIAL SITUATIONS 357stati
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8.5 UBIQUITOUS DATA MINING 359ing p
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8.6 FURTHER READING 361to contain a
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partIIThe Weka Machine LearningWork
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366 CHAPTER 9 | INTRODUCTION TO WEK
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368 CHAPTER 9 | INTRODUCTION TO WEK
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370 CHAPTER 10 | THE EXPLORERFigure
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372 CHAPTER 10 | THE EXPLORER(a)(b)
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376 CHAPTER 10 | THE EXPLORER=== De
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378 CHAPTER 10 | THE EXPLORERright-
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380 CHAPTER 10 | THE EXPLORER10.2 E
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382 CHAPTER 10 | THE EXPLORERFiles
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384 CHAPTER 10 | THE EXPLORERFigure
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=== Run information ===Scheme: weka
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392 CHAPTER 10 | THE EXPLORERmethod
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396 CHAPTER 10 | THE EXPLORERNameTa
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404 CHAPTER 10 | THE EXPLORERTable
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422 CHAPTER 10 | THE EXPLORERnation
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chapter 11The Knowledge Flow Interf
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11.1 GETTING STARTED 429(a)(b)Figur
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11.3 CONFIGURING AND CONNECTING THE
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11.4 INCREMENTAL LEARNING 433pervis
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11.4 INCREMENTAL LEARNING 435When t
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438 CHAPTER 12 | THE EXPERIMENTER12
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440 CHAPTER 12 | THE EXPERIMENTERsc
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442 CHAPTER 12 | THE EXPERIMENTERal
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444 CHAPTER 12 | THE EXPERIMENTER(a
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chapter 13The Command-line Interfac
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13.2 THE STRUCTURE OF WEKA 451Large
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The weka.classifiers packageThe cla
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13.2 THE STRUCTURE OF WEKA 455Figur
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13.3 COMMAND-LINE OPTIONS 457Table
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13.3 COMMAND-LINE OPTIONS 459proced
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462 CHAPTER 14 | EMBEDDED MACHINE L
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chapter 15Writing New Learning Sche
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15.1 AN EXAMPLE CLASSIFIER 473packa
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15.1 AN EXAMPLE CLASSIFIER 475m_Cla
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15.1 AN EXAMPLE CLASSIFIER 477* @re
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15.1 AN EXAMPLE CLASSIFIER 479** @p
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15.1 AN EXAMPLE CLASSIFIER 481for b
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15.2 CONVENTIONS FOR IMPLEMENTING C
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486 REFERENCESAsmis, E. 1984. Epicu
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488 REFERENCESCavnar, W. B., and J.
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490 REFERENCESDrucker, H. 1997. Imp
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492 REFERENCESFreund, Y., and L. Ma
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494 REFERENCESHall, M., G. Holmes,
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496 REFERENCESKohavi, R. 1995a. A s
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498 REFERENCESMann, T. 1993. Librar
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500 REFERENCESHeckerman, H. Mannila
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502 REFERENCESStone, P., and M. Vel
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IndexAactivation function, 234acuit
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INDEX 507automatic filtering, 315av
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INDEX 509randomization, 320-321stac
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INDEX 511document classification, 9
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INDEX 513Fisher, R. A., 15flat file
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INDEX 515KK2, 278Kappa statistic, 1
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INDEX 517multiclass alternating dec
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INDEX 519predicting performance, 14
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INDEX 521sigmoid kernel, 219Simple
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INDEX 523unlabeled data, 337-341clu
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About the AuthorsIan H. Witten is a
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