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

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List of FiguresFigure 1.1 Rules for the contact lens data. 13Figure 1.2 Decision tree for the contact lens data. 14Figure 1.3 Decision trees for the labor negotiations data. 19Figure 2.1 A family tree and two ways of expressing the sister-ofrelation. 46Figure 2.2 ARFF file for the weather data. 54Figure 3.1Constructing a decision tree interactively: (a) creating arectangular test involving petallength and petalwidth and (b)the resulting (unfinished) decision tree. 64Figure 3.2 Decision tree for a simple disjunction. 66Figure 3.3 The exclusive-or problem. 67Figure 3.4 Decision tree with a replicated subtree. 68Figure 3.5 Rules for the Iris data. 72Figure 3.6 The shapes problem. 73Figure 3.7Models for the CPU performance data: (a) linear regression,(b) regression tree, and (c) model tree. 77Figure 3.8 Different ways of partitioning the instance space. 79Figure 3.9 Different ways of representing clusters. 81Figure 4.1 Pseudocode for 1R. 85Figure 4.2 Tree stumps for the weather data. 98Figure 4.3 Expanded tree stumps for the weather data. 100Figure 4.4 Decision tree for the weather data. 101Figure 4.5 Tree stump for the ID code attribute. 103Figure 4.6 Covering algorithm: (a) covering the instances and (b) thedecision tree for the same problem. 106Figure 4.7 The instance space during operation of a coveringalgorithm. 108Figure 4.8 Pseudocode for a basic rule learner. 111Figure 4.9Logistic regression: (a) the logit transform and (b) an examplelogistic regression function. 122xvii
xviiiLIST OF FIGURESFigure 4.10 The perceptron: (a) learning rule and (b) representation asa neural network. 125Figure 4.11 The Winnow algorithm: (a) the unbalanced version and (b)the balanced version. 127Figure 4.12 A kD-tree for four training instances: (a) the tree and (b)instances and splits. 130Figure 4.13 Using a kD-tree to find the nearest neighbor of thestar. 131Figure 4.14 Ball tree for 16 training instances: (a) instances and balls and(b) the tree. 134Figure 4.15 Ruling out an entire ball (gray) based on a target point (star)and its current nearest neighbor. 135Figure 4.16 A ball tree: (a) two cluster centers and their dividing line and(b) the corresponding tree. 140Figure 5.1 A hypothetical lift chart. 168Figure 5.2 A sample ROC curve. 169Figure 5.3 ROC curves for two learning methods. 170Figure 5.4 Effects of varying the probability threshold: (a) the error curveand (b) the cost curve. 174Figure 6.1 Example of subtree raising, where node C is “raised” tosubsume node B. 194Figure 6.2 Pruning the labor negotiations decision tree. 196Figure 6.3 Algorithm for forming rules by incremental reduced-errorpruning. 205Figure 6.4 RIPPER: (a) algorithm for rule learning and (b) meaning ofFigure 6.5symbols. 206Algorithm for expanding examples into a partialtree. 208Figure 6.6 Example of building a partial tree. 209Figure 6.7 Rules with exceptions for the iris data. 211Figure 6.8 A maximum margin hyperplane. 216Figure 6.9 Support vector regression: (a) e = 1, (b) e = 2, and (c)e = 0.5. 221Figure 6.10 Example datasets and corresponding perceptrons. 225Figure 6.11Step versus sigmoid: (a) step function and (b) sigmoidfunction. 228Figure 6.12 Gradient descent using the error function x 2 + 1. 229Figure 6.13 Multilayer perceptron with a hidden layer. 231Figure 6.14 A boundary between two rectangular classes. 240Figure 6.15 Pseudocode for model tree induction. 248Figure 6.16 Model tree for a dataset with nominal attributes. 250Figure 6.17 Clustering the weather data. 256
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
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34 CHAPTER 1 | WHAT’S IT ALL ABOU
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chapter 2Input:Concepts, Instances,
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2.1 WHAT’S A CONCEPT? 43increase
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2.2 WHAT’S IN AN EXAMPLE? 45data
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2.2 WHAT’S IN AN EXAMPLE? 47Table
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2.3 WHAT’S IN AN ATTRIBUTE? 49Tab
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2.3 WHAT’S IN AN ATTRIBUTE? 51Not
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2.4 PREPARING THE INPUT 53cleaned u
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missing values in this dataset). Th
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dividing by the range between the m
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2.4 PREPARING THE INPUT 59a record
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chapter 3Output:Knowledge Represent
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3.2 DECISION TREES 63Alternatively,
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3.3 CLASSIFICATION RULES 65nated by
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3.3 CLASSIFICATION RULES 671abx = 1
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3.4 ASSOCIATION RULES 69yes, then i
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3.5 RULES WITH EXCEPTIONS 71If peta
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3.6 RULES INVOLVING RELATIONS 73ica
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Standard relations include equality
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PRP =-56.1+0.049 MYCT+0.015 MMIN+0.
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3.8 INSTANCE-BASED REPRESENTATION 7
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3.9 Clusters3.9 CLUSTERS 81When clu
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chapter 4Algorithms:The Basic Metho
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4.1 INFERRING RUDIMENTARY RULES 85F
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described overfitting-avoidance bia
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4.2 STATISTICAL MODELING 89Table 4.
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just as we calculated previously. A
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4.2 STATISTICAL MODELING 93Table 4.
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of a document. Instead, a document
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4.3 DIVIDE-AND-CONQUER: CONSTRUCTIN
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4.4 COVERING ALGORITHMS: CONSTRUCTI
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4.5 MINING ASSOCIATION RULES 113acc
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4.5 MINING ASSOCIATION RULES 115Tab
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4.5 MINING ASSOCIATION RULES 117whi
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4.6 LINEAR MODELS 119through the da
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4.6 LINEAR MODELS 121However, linea
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4.6 LINEAR MODELS 123n( i)i iÂ 1-x
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4.6 LINEAR MODELS 125Set all weight
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4.6 LINEAR MODELS 127While some ins
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4.7 INSTANCE-BASED LEARNING 129When
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4.7 INSTANCE-BASED LEARNING 131Figu
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4.8 CLUSTERING 137As we saw in Sect
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4.9 FURTHER READING 139can be updat
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4.9 FURTHER READING 141Bayes was an
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chapter 5Credibility:Evaluating Wha
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5.1 TRAINING AND TESTING 145of each
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ather than error rate, so this corr
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5.3 CROSS-VALIDATION 149mediate con
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5.4 OTHER ESTIMATES 151A single 10-
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90% used in 10-fold cross-validatio
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5.5 COMPARING DATA MINING METHODS 1
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5.6 PREDICTING PROBABILITIES 157In
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5.6 PREDICTING PROBABILITIES 159whe
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mental job expected of a loss funct
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y the total number of positives, wh
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5.7 COUNTING THE COST 165different
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5.7 COUNTING THE COST 167Table 5.6D
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5.7 COUNTING THE COST 169100%80%tru
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5.7 COUNTING THE COST 171should cho
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Different terms are used in differe
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5.7 COUNTING THE COST 1750.5Anormal
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5.8 EVALUATING NUMERIC PREDICTION 1
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5.9 THE MINIMUM DESCRIPTION LENGTH
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5.9 THE MINIMUM DESCRIPTION LENGTH
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5.10 APPLYING THE MDL PRINCIPLE TO
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5.11 FURTHER READING 185tion theory
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188 CHAPTER 6 | IMPLEMENTATIONS: RE
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chapter 7Transformations:Engineerin
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7.1 ATTRIBUTE SELECTION 287attribut
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7.1 ATTRIBUTE SELECTION 289and less
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tion—and it is much easier to und
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7.1 ATTRIBUTE SELECTION 293outlook
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7.1 ATTRIBUTE SELECTION 295the t-te
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7.2 DISCRETIZING NUMERIC ATTRIBUTES
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7.3 SOME USEFUL TRANSFORMATIONS 305
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7.4 AUTOMATIC DATA CLEANSING 313Int
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7.5 COMBINING MULTIPLE MODELS 315da
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7.5 COMBINING MULTIPLE MODELS 317pa
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7.5 COMBINING MULTIPLE MODELS 319mo
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7.5 COMBINING MULTIPLE MODELS 321Ra
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7.5 COMBINING MULTIPLE MODELS 323Ho
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7.5 COMBINING MULTIPLE MODELS 325Th
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7.5 COMBINING MULTIPLE MODELS 327of
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7.5 COMBINING MULTIPLE MODELS 329ou
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7.5 COMBINING MULTIPLE MODELS 331ev
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7.5 COMBINING MULTIPLE MODELS 333be
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7.5 COMBINING MULTIPLE MODELS 335Ta
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7.6 Using unlabeled data7.6 USING U
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7.6 USING UNLABELED DATA 339automat
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7.7 FURTHER READING 341deal with we
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7.7 FURTHER READING 343Domingos (19
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346 CHAPTER 8 | MOVING ON: EXTENSIO
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chapter 9Introduction to WekaExperi
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9.2 How do you use it?9.2 HOW DO YO
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chapter 10The ExplorerWeka’s main
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10.1 GETTING STARTED 371(a)(b)(c)Fi
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10.1 GETTING STARTED 373deviation.
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10.1 GETTING STARTED 375=== Run inf
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10.1 GETTING STARTED 377Doing it ag
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10.1 GETTING STARTED 379(a)(b)Figur
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10.2 EXPLORING THE EXPLORER 381(a)(
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10.2 EXPLORING THE EXPLORER 383(b)(
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10.2 EXPLORING THE EXPLORER 385(a)(
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10.2 EXPLORING THE EXPLORER 387+ 0.
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10.2 EXPLORING THE EXPLORER 389posi
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10.2 EXPLORING THE EXPLORER 391Figu
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10.3 FILTERING ALGORITHMS 393method
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10.3 FILTERING ALGORITHMS 395(b)Fig
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10.3 FILTERING ALGORITHMS 397or fir
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10.3 FILTERING ALGORITHMS 399attrib
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10.3 FILTERING ALGORITHMS 401it int
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10.4 LEARNING ALGORITHMS 403There i
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10.4 LEARNING ALGORITHMS 405Table 1
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10.4 LEARNING ALGORITHMS 407Figure
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10.4 LEARNING ALGORITHMS 409value (
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10.4 LEARNING ALGORITHMS 411Neural
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10.4 LEARNING ALGORITHMS 413there a
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10.5 METALEARNING ALGORITHMS 415Tab
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10.5 METALEARNING ALGORITHMS 417Com
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10.7 ASSOCIATION-RULE LEARNERS 419N
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10.8 ATTRIBUTE SELECTION 421Table 1
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10.8 ATTRIBUTE SELECTION 423tribute
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10.8 ATTRIBUTE SELECTION 425with on
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428 CHAPTER 11 | THE KNOWLEDGE FLOW
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chapter 12The ExperimenterThe Explo
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12.1 GETTING STARTED 439Dataset,Run
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12.2 SIMPLE SETUP 441cance test of
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12.4 THE ANALYZE PANEL 443advanced
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12.5 DISTRIBUTING PROCESSING OVER S
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12.5 DISTRIBUTING PROCESSING OVER S
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450 CHAPTER 13 | THE COMMAND-LINE I
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chapter 14Embedded Machine Learning
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14.2 GOING THROUGH THE CODE 463/***
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14.2 GOING THROUGH THE CODE 465// I
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14.2 GOING THROUGH THE CODE 467}//
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14.2 GOING THROUGH THE CODE 469filt
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472 CHAPTER 15 | WRITING NEW LEARNI
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ReferencesAdriaans, P., and D. Zant
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Bouckaert, R. R. 2004. Bayesian net
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REFERENCES 489Cypher, A., editor. 1
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REFERENCES 491Fix, E., and J. L. Ho
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REFERENCES 493Gennari, J. H., P. La
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Conference on Knowledge Discovery a
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REFERENCES 497Kushmerick, N., D. S.
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Moore, A. W., and M. S. Lee. 1994.
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REFERENCES 501editor, Proceedings o
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REFERENCES 503Webb, G. I., J. Bough
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506 INDEXanomaly detection systems,
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508 INDEXcausal relations, 350CfsSu
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510 INDEXCSVLoader, 381cumulative m
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512 INDEXExperimenter, 437-447advan
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514 INDEXimplementation—real-worl
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516 INDEXlistOptions(), 482literary
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518 INDEXnumeric prediction (contin
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520 INDEXrelational data, 49relatio
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522 INDEXsupport vector, 216support
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524 INDEXWinnow, 410Winnow algorith
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