Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
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| List of Figures Figure 1.1 Rules for the contact lens data. 13 Figure 1.2 Decision tree for the contact lens data. 14 Figure 1.3 Decision trees for the labor negotiations data. 19 Figure 2.1 A family tree and two ways of expressing the sister-of relation. 46 Figure 2.2 ARFF file for the weather data. 54 Figure 3.1 Constructing a decision tree interactively: (a) creating a rectangular test involving petallength and petalwidth and (b) the resulting (unfinished) decision tree. 64 Figure 3.2 Decision tree for a simple disjunction. 66 Figure 3.3 The exclusive-or problem. 67 Figure 3.4 Decision tree with a replicated subtree. 68 Figure 3.5 Rules for the Iris data. 72 Figure 3.6 The shapes problem. 73 Figure 3.7 Models for the CPU performance data: (a) linear regression, (b) regression tree, and (c) model tree. 77 Figure 3.8 Different ways of partitioning the instance space. 79 Figure 3.9 Different ways of representing clusters. 81 Figure 4.1 Pseudocode for 1R. 85 Figure 4.2 Tree stumps for the weather data. 98 Figure 4.3 Expanded tree stumps for the weather data. 100
Figure 4.4 Decision tree for the weather data. 101 Figure 4.5 Tree stump for the ID code attribute. 103
Figure 4.6 Covering algorithm: (a) covering the instances and (b) the decision tree for the same problem. 106
Figure 4.7 The instance space during operation of a covering algorithm. 108
Figure 4.8 Pseudocode for a basic rule learner. 111 Figure 4.9 Logistic regression: (a) the logit transform and (b) an example logistic regression function. 122 x v i i
P088407-FM.qxd 4/30/05 10:55 AM Page xvii Figure 4.10 The perceptron: (a) learning rule and (b) representation as a neural network. 125
Figure 4.11 The Winnow algorithm: (a) the unbalanced version and (b) the balanced version. 127
Figure 4.12 A kD-tree for four training instances: (a) the tree and (b) instances and splits. 130
Figure 4.13 Using a kD-tree to find the nearest neighbor of the star. 131
Figure 4.14 Ball tree for 16 training instances: (a) instances and balls and (b) the tree. 134
Figure 4.15 Ruling out an entire ball (gray) based on a target point (star) and its current nearest neighbor. 135
Figure 4.16 A ball tree: (a) two cluster centers and their dividing line and (b) the corresponding tree. 140
Figure 5.1 A hypothetical lift chart. 168 Figure 5.2 A sample ROC curve. 169
Figure 5.3 ROC curves for two learning methods. 170 Figure 5.4 Effects of varying the probability threshold: (a) the error curve and (b) the cost curve. 174 Figure 6.1 Example of subtree raising, where node C is “raised” to subsume node B. 194 Figure 6.2 Pruning the labor negotiations decision tree. 196
Figure 6.3 Algorithm for forming rules by incremental reduced-error pruning. 205
Figure 6.4 RIPPER: (a) algorithm for rule learning and (b) meaning of symbols. 206
Figure 6.5 Algorithm for expanding examples into a partial tree. 208
Figure 6.6 Example of building a partial tree. 209 Figure 6.7 Rules with exceptions for the iris data. 211
Figure 6.8 A maximum margin hyperplane. 216 Figure 6.9 Support vector regression: (a) e = 1, (b) e = 2, and (c) e = 0.5. 221
Figure 6.10 Example datasets and corresponding perceptrons. 225 Figure 6.11 Step versus sigmoid: (a) step function and (b) sigmoid function. 228 Figure 6.12 Gradient descent using the error function x 2 + 1. 229 Figure 6.13 Multilayer perceptron with a hidden layer. 231
Figure 6.14 A boundary between two rectangular classes. 240 Figure 6.15 Pseudocode for model tree induction. 248
Figure 6.16 Model tree for a dataset with nominal attributes. 250 Figure 6.17 Clustering the weather data. 256
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Figure 6.18 Hierarchical clusterings of the iris data. 259 Figure 6.19 A two-class mixture model. 264
Figure 6.20 A simple Bayesian network for the weather data. 273 Figure 6.21 Another Bayesian network for the weather data. 274
Figure 6.22 The weather data: (a) reduced version and (b) corresponding AD tree. 281
Figure 7.1 Attribute space for the weather dataset. 293 Figure 7.2 Discretizing the temperature attribute using the entropy method.
299 Figure 7.3 The result of discretizing the temperature attribute. 300
Figure 7.4 Class distribution for a two-class, two-attribute problem. 303
Figure 7.5 Principal components transform of a dataset: (a) variance of each component and (b) variance plot. 308
Figure 7.6 Number of international phone calls from Belgium, 1950–1973. 314
Figure 7.7 Algorithm for bagging. 319 Figure 7.8 Algorithm for boosting. 322
Figure 7.9 Algorithm for additive logistic regression. 327 Figure 7.10 Simple option tree for the weather data. 329
Figure 7.11 Alternating decision tree for the weather data. 330 Figure 10.1 The Explorer interface. 370
Figure 10.2 Weather data: (a) spreadsheet, (b) CSV format, and (c) ARFF. 371
Figure 10.3 The Weka Explorer: (a) choosing the Explorer interface and (b) reading in the weather data. 372
Figure 10.4 Using J4.8: (a) finding it in the classifiers list and (b) the Classify tab. 374
Figure 10.5 Output from the J4.8 decision tree learner. 375 Figure 10.6 Visualizing the result of J4.8 on the iris dataset: (a) the tree and (b) the classifier errors. 379 Figure 10.7 Generic object editor: (a) the editor, (b) more information (click More), and (c) choosing a converter (click Choose). 381
Figure 10.8 Choosing a filter: (a) the filters menu, (b) an object editor, and (c) more information (click More). 383
Figure 10.9 The weather data with two attributes removed. 384 Figure 10.10 Processing the CPU performance data with M5 ¢. 385 Figure 10.11 Output from the M5 ¢ program for numeric prediction. 386 Figure 10.12 Visualizing the errors: (a) from M5 ¢ and (b) from linear regression. 388
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