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• Classic-Fiction

challenges426

categorical attributes41

CBA. seeClassification Based on Associations

CBLOF. seecluster-based local outlier factor

CELL method562, 563

cells10–11

aggregate189

ancestor189

base189

descendant189

dimensional189

exceptions231

residual value234

central tendency measures39, 44, 45–47

mean45–46

median46–47

midrange47

for missing values88

models47

centroid distance108

CF-trees462–463, 464

nodes465

parameters464

structure illustration464

CHAID343

Chameleon459, 466–467

clustering illustration466

relative closeness467

relative interconnectivity466–467 see alsohierarchical methods

Chernoff faces60

asymmetrical61

illustrated62

ChiMerge117

chi-square test95

chunking195

chunks195

2-D197

3-D197

computation of198

scanning order197

CLARA. seeClustering Large Applications

CLARANS. seeClustering Large Applications based upon Randomized Search

class comparisons166, 175, 180

attribute-oriented induction for175–178

mining176

presentation of175–176

procedure175–176

class conditional independence350

class imbalance problem384–385, 386

ensemble methods for385

on multiclass tasks385

oversampling384–385, 386

threshold-moving approach385

undersampling384–385, 386

class label attributes328

class-based ordering357

class/concept descriptions15

classes15, 166

contrasting15

equivalence427

target15

classification18, 327–328, 385

accuracy330

accuracy improvement techniques377–385

active learning433–434

advanced methods393–442

applications327

associative415, 416–419, 437

automatic445

backpropagation393, 398–408, 437

bagging379–380

basic concepts327–330

Bayes methods350–355

Bayesian belief networks393–397, 436

boosting380–382

case-based reasoning425–426

of class-imbalanced data383–385

confusion matrix365–366, 386

costs and benefits373–374

decision tree induction330–350

discriminative frequent pattern-based437

document430

ensemble methods378–379

evaluation metrics364–370

example19

frequent pattern-based393, 415–422, 437

fuzzy set approaches428–429, 437

general approach to328

genetic algorithms426–427, 437

heterogeneous networks593

homogeneous networks593

IF-THEN rules for355–357

interpretability369

k-nearest-neighbor423–425

lazy learners393, 422–426

learning step328

model representation18

model selection364, 370–377

multiclass430–432, 437

in multimedia data mining596

neural networks for19, 398–408

pattern-based282, 318

perception-based348–350

precision measure368–369

as prediction problem328

process328

process illustration329

random forests382–383

recall measure368–369

robustness369

rough set approach427–428, 437

rule-based355–363, 386

scalability369

semi-supervised432–433, 437

sentiment434

spatial595

speed369

support vector machines (SVMs)393, 408–415, 437

transfer learning434–436

tree pruning344–347, 385

web-document435

Classification Based on Associations (CBA)417

Classification based on Multiple Association Rules (CMAR)417–418

Classification based on Predictive Association Rules (CPAR)418–419

classification-based outlier detection571–573, 582

one-class model571–572

semi-supervised learning572 see alsooutlier detection

classifiers328

accuracy330, 366

bagged379–380

Bayesian350, 353

case-based reasoning425–426

comparing with ROC curves373–377

comparison aspects369

decision tree331

error rate367

k-nearest-neighbor423–425

Naive Bayesian351–352

overfitting data330

performance evaluation metrics364–370

recognition rate366–367

rule-based355

Clementine603, 606

CLIQUE481–483

clustering steps481–482

effectiveness483

strategy481 see alsocluster analysis; grid-based methods

closed data cubes192

closed frequent itemsets247, 308

example248

mining262–264

shortcomings for compression308–309

closed graphs591

closed patterns280

top-k most frequent307