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• Classic-Fiction

combined significance312

complete-linkage algorithm462

completeness

data84–85

data mining algorithm22

complex data types166

biological sequence data586, 590–591

graph patterns591–592

mining585–598, 625

networks591–592

in science applications612

summary586

symbolic sequence data586, 588–590

time-series data586, 587–588

composite join indices162

compressed patterns281

mining307–312

mining by pattern clustering308–310

compression100, 120

lossless100

lossy100

theory601

computer science applications613

concept characterization180

concept comparison180

concept description166, 180

concept hierarchies142, 179

for generalizing data150

illustrated143, 144

implicit143

manual provision144

multilevel association rule mining with285

multiple144

for nominal attributes284

for specializing data150

concept hierarchy generation112, 113, 120

based on number of distinct values118

illustrated112

methods117–119

for nominal data117–119

with prespecified semantic connections119

schema119

conditional probability table (CPT)394, 395–396

confidence21

association rule21

interval219–220

limits373

rule245, 246

conflict resolution strategy356

confusion matrix365–366, 386

illustrated366

connectionist learning398

consecutive rules92

Constrained Vector Quantization Error (CVQE) algorithm536

constraint-based clustering447, 497, 532–538, 539

categorization of constraints and533–535

hard constraints535–536

methods535–538

soft constraints536–537

speeding up537–538 see alsocluster analysis

constraint-based mining294–301, 320

interactive exploratory mining/analysis295

as mining trend623

constraint-based patterns/rules281

constraint-based sequential pattern mining589

constraint-guided mining30

constraints

antimonotonic298, 301

association rule296–297

cannot-link533

on clusters533

coherence535

conflicting535

convertible299–300

data294

data-antimonotonic300

data-pruning300–301, 320

data-succinct300

dimension/level294, 297

hard534, 535–536, 539

inconvertible300

on instances533, 539

interestingness294, 297

knowledge type294

monotonic298

must-link533, 536

pattern-pruning297–300, 320

rules for294

on similarity measures533–534

soft534, 536–537, 539

succinct298–299

content-based retrieval596

context indicators314

context modeling316

context units314

contextual attributes546, 573

contextual outlier detection546–547, 582

with identified context574

normal behavior modeling574–575

structures as contexts575

summary575

transformation to conventional outlier detection573–574

contextual outliers545–547, 573, 581

example546, 573

mining573–575

contingency tables95

continuous attributes44

contrasting classes15, 180

initial working relations177

prime relation175, 177

convertible constraints299–300

COP k-means algorithm536

core descendants305

colossal patterns306

merging of core patterns306

core patterns304–305

core ratio305

correlation analysis94

discretization by117

interestingness measures264

with lift266–267

nominal data95–96

numeric data96–97

redundancy and94–98

correlation coefficient94, 96

numeric data96–97

correlation rules265, 272

correlation-based clustering methods511

correlations18

cosine measure268

cosine similarity77

between two term-frequency vectors78

cost complexity pruning algorithm345

cotraining432–433

covariance94, 97

numeric data97–98

CPAR. seeClassification based on Predictive Association Rules

credit policy analysis608–609

CRM. seecustomer relationship management

crossover operation426

cross-validation370–371, 386

k-fold370

leave-one-out371

in number of clusters determination487

stratified371

cube gradient analysis321

cube shells192, 211

computing211

cube space

discovery-driven exploration231–234

multidimensional data analysis in227–234

prediction mining in227

subspaces228–229

cuboid trees205

cuboids137

apex111, 138, 158