• Choose a category
• All
• Classic-Fiction

grid-based562–564

types of552, 560 see alsooutlier detection

pruning

cost complexity algorithm345

data space300–301

decision trees331, 344–347

in k-nearest neighbor classification425

network406–407

pattern space295, 297–300

pessimistic345

postpruning344–345, 346

prepruning344, 346

rule363

search space263, 301

sets345

shared dimensions205

sub-itemset263

pyramid algorithm101

Q

quality control600

quantile plots51–52

quantile-quantile plots52

example53–54

illustrated53 see alsographic displays

quantitative association rules281, 283, 288, 320

clustering-based mining290–291

data cube-based mining289–290

exceptional behavior disclosure291

mining289

quartiles48

first49

third49

queries10

intercuboid expansion223–225

intracuboid expansion221–223

language10

OLAP129, 130

point216, 217, 220

processing163–164, 218–227

range220

relational operations10

subcube216, 217–218

top-k225–227

query languages31

query models149–150

query-driven approach128

querying function433

R

rag bag criterion488

RainForest385

random forests382–383

random sampling370, 386

random subsampling370

random walk526

similarity based on527

randomization methods621

range48

interquartile49

range queries220

ranking

cubes225–227, 235

dimensions225

function225

heterogeneous networks593

rare patterns280, 283, 320

example291–292

mining291–294

ratio-scaled attributes43–44, 79

reachability density566

reachability distance565

recall measure368–369

recognition rate366–367

recommender systems282, 615

advantages616

biclustering for514–515

challenges617

collaborative610, 615, 616, 617, 618

content-based approach615, 616

data mining and615–618

error types617–618

frequent pattern mining for319

hybrid approaches618

intelligent query answering618

memory-based methods617

use scenarios616

recursive partitioning335

reduced support285, 286

redundancy

in data integration94

detection by correlations analysis94–98

redundancy-aware top-k patterns281, 311, 320

extracting310–312

finding312

strategy comparison311–312

trade-offs312

refresh, in back-end tools/utilities134

regression19, 90

coefficients105–106

example19

linear90, 105–106

in statistical data mining599

regression analysis19, 328

in time-series data587–588

relational databases9

components of9

mining10

relational schema for10

relational OLAP (ROLAP)132, 164, 165, 179

relative significance312

relevance analysis19

repetition346

replication347

illustrated346

representative patterns309

retail industry609–611

RIPPER359, 363

robustness, classification369

ROC curves374, 386

classification models377

classifier comparison with373–377

illustrated376, 377

plotting375

roll-up operation11, 146

rough set approach428–429, 437

row enumeration302

rule ordering357

rule pruning363

rule quality measures361–363

rule-based classification355–363, 386

IF-THEN rules355–357

rule extraction357–359

rule induction359–363

rule pruning363

rule quality measures361–363

rules for constraints294

S

sales campaign analysis610

samples218

cluster108–109

data219

simple random108

stratified109–110

sampling

in Apriori efficiency256

as data redundancy technique108–110

methods108–110

oversampling384–385

random386

with replacement380–381

uncertainty433

undersampling384–385

sampling cubes218–220, 235

confidence interval219–220

framework219–220

query expansion with221

SAS Enterprise Miner603, 604

scalability

classification369

cluster analysis446

cluster methods445

data mining algorithms31

decision tree induction and347–348

dimensionality and577

k-means454

scalable computation319

SCAN. seeStructural Clustering Algorithm for Networks

core vertex531

illustrated532

scatter plots54

2-D data set visualization with59

3-D data set visualization with60

correlations between attributes54–56

illustrated55

matrix56,