Data Mining: Practical Machine Learning Tools and Techniques, Second Edition

Figure 10.13

Working on the segmentation data with the User Classifier:

(a) the data visualizer and (b) the tree visualizer.

390

Figure 10.14

Configuring a metalearner for boosting decision 

stumps.

391

Figure 10.15

Output from the Apriori program for association rules.

392

Figure 10.16

Visualizing the Iris dataset.

394

Figure 10.17

Using Weka’s metalearner for discretization: (a) configuring

FilteredClassifier, and (b) the menu of filters.

402

Figure 10.18

Visualizing a Bayesian network for the weather data (nominal

version): (a) default output, (b) a version with the

maximum number of parents set to 3 in the search

algorithm, and (c) probability distribution table for the

windy node in (b).

406

Figure 10.19

Changing the parameters for J4.8.

407

Figure 10.20

Using Weka’s neural-network graphical user 

interface.

411

Figure 10.21

Attribute selection: specifying an evaluator and a search

method.

420

Figure 11.1

The Knowledge Flow interface.

428

Figure 11.2

Configuring a data source: (a) the right-click menu and 

(b) the file browser obtained from the Configure menu 

item.

429

Figure 11.3

Operations on the Knowledge Flow components.

432

Figure 11.4

A Knowledge Flow that operates incrementally: (a) the

configuration and (b) the strip chart output.

434

Figure 12.1

An experiment: (a) setting it up, (b) the results file, and 

(c) a spreadsheet with the results.

438

Figure 12.2

Statistical test results for the experiment in 

Figure 12.1.

440

Figure 12.3

Setting up an experiment in advanced mode.

442

Figure 12.4

Rows and columns of Figure 12.2: (a) row field, (b) column

field, (c) result of swapping the row and column selections,

and (d) substituting Run for Dataset as rows.

444

Figure 13.1

Using Javadoc: (a) the front page and (b) the weka.core

package.

452

Figure 13.2

DecisionStump: A class of the weka.classifiers.trees

package.

454

Figure 14.1

Source code for the message classifier.

463

Figure 15.1

Source code for the ID3 decision tree learner.

473

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List of Tables

Table 1.1

The contact lens data.

6

Table 1.2

The weather data.

11

Table 1.3

Weather data with some numeric attributes.

12

Table 1.4

The iris data.

15

Table 1.5

The CPU performance data.

16

Table 1.6

The labor negotiations data.

18

Table 1.7

The soybean data.

21

Table 2.1

Iris data as a clustering problem.

44

Table 2.2

Weather data with a numeric class.

44

Table 2.3

Family tree represented as a table.

47

Table 2.4

The sister-of relation represented in a table.

47

Table 2.5

Another relation represented as a table.

49

Table 3.1

A new iris flower.

70

Table 3.2

Training data for the shapes problem.

74

Table 4.1

Evaluating the attributes in the weather data.

85

Table 4.2

The weather data with counts and probabilities.

89

Table 4.3

A new day.

89

Table 4.4

The numeric weather data with summary statistics.

93

Table 4.5

Another new day.

94

Table 4.6

The weather data with identification codes.

103

Table 4.7

Gain ratio calculations for the tree stumps of Figure 4.2.

104

Table 4.8

Part of the contact lens data for which astigmatism 

= yes.

109

Table 4.9

Part of the contact lens data for which astigmatism 

= yes and

tear production rate 

= normal.

110

Table 4.10

Item sets for the weather data with coverage 2 or 

greater.

114

Table 4.11

Association rules for the weather data.

116

Table 5.1

Confidence limits for the normal distribution.

148

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Table 5.2

Confidence limits for Student’s distribution with 9 degrees 

of freedom.

155

Table 5.3

Different outcomes of a two-class prediction.

162

Table 5.4

Different outcomes of a three-class prediction: (a) actual and

(b) expected.

163

Table 5.5

Default cost matrixes: (a) a two-class case and (b) a three-class

case.

164

Table 5.6

Data for a lift chart.

167

Table 5.7

Different measures used to evaluate the false positive versus the

false negative tradeoff.

172

Table 5.8

Performance measures for numeric prediction.

178

Table 5.9

Performance measures for four numeric prediction 

models.

179

Table 6.1

Linear models in the model tree.

250

Table 7.1

Transforming a multiclass problem into a two-class one:

(a) standard method and (b) error-correcting code.

335

Table 10.1

Unsupervised attribute filters.

396

Table 10.2

Unsupervised instance filters.

400

Table 10.3

Supervised attribute filters.

402

Table 10.4

Supervised instance filters.

402

Table 10.5

Classifier algorithms in Weka.

404

Table 10.6

Metalearning algorithms in Weka.

415

Table 10.7

Clustering algorithms.

419

Table 10.8

Association-rule learners.

419

Table 10.9

Attribute evaluation methods for attribute selection.

421

Table 10.10

Search methods for attribute selection.

421

Table 11.1

Visualization and evaluation components.

430

Table 13.1

Generic options for learning schemes in Weka.

457

Table 13.2

Scheme-specific options for the J4.8 decision tree 

learner.

458

Table 15.1

Simple learning schemes in Weka.

472

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