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

(Brent) #1

512 INDEX


Experimenter, 437–447
advanced panel, 443–445
Analyzepanel, 443–445
analyzing the results, 440–441
distributing processing over several
machines, 445–447
running an experiment, 439–440
simple setup, 441–442
starting up, 438–441
subexperiments, 447
Explorer, 369–425
ARFF, 370, 371, 380–382
Associatepanel, 392
association-rule learners, 419–420
attribute evaluation methods, 421,
422–423
attribute selection, 392–393, 420–425
boosting, 416
classifier errors, 379
Classifypanel, 384
clustering algorithms, 418–419
Clusterpanel, 391–392
CSV format, 370, 371
error log, 378
file format converters, 380–382
filtering algorithms, 393–403
filters, 382–384
J4.8, 373–377
learning algorithms, 403–414.See also
learning algorithms
metalearning algorithms, 414–418
models, 377–378
panels, 380
Preprocesspanel, 380
search methods, 421, 423–425
Select attributespanel, 392–393
starting up, 369–379
supervised filters, 401–403
training/testing learning schemes,
384–387
unsupervised attribute filters, 395–400
unsupervised instance filters, 400–401
User Classifier, 388–391
Visualizepanel, 393
extraction problems, 353, 354

F
Fahrenheit, Daniel, 51
fallback heuristic, 239
false negative (FN), 162
false positive (FP), 162
false positive rate, 163
False positive rate, 378
Familiar, 360
family tree, 45
tabular representation of, 46
FarthestFirst, 419
features. See attributes
feature selection, 341.See alsoattribute
selection
feedforward networks, 233
fielded applications, 22
continuous monitoring, 28–29
customer support and service, 28
cybersecurity, 29
diagnosis, 25–26
ecological applications, 23, 28
electricity supply, 24–25
hazard detection system, 23–24
load forecasting, 24–25
loan application, 22–23
manufacturing processes, 28
marketing and sales, 26–28
oil slick detection, 23
preventive maintenance of
electromechanical devices, 25–26
scientific applications, 28
file format converters, 380–382
file mining, 49
filter, 290
filter in Weka, 382–384
FilteredClassifier, 401, 414
filtering algorithms in Weka, 393–403
sparse instances, 401
supervised filters, 401–403
unsupervised attribute filters, 395–400
unsupervised instance filters, 400–401
filtering approaches, 315
filters menu, 383
finite mixture, 262, 263
FirstOrder, 399

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