• Choose a category
• All
• Classic-Fiction

schemas

integration94

snowflake140–141

star139–140

science applications611–613

search engines28

search space pruning263, 301

second guess heuristic369

selection dimensions225

self-training432

semantic annotations

applications317, 313, 320–321

with context modeling316

from DBLP data set316–317

effectiveness317

example314–315

of frequent patterns313–317

mutual information315–316

task definition315

Semantic Web597

semi-offline materialization226

semi-supervised classification432–433, 437

alternative approaches433

cotraining432–433

self-training432

semi-supervised learning25

outlier detection by572

semi-supervised outlier detection551

sensitivity analysis408

sensitivity measure367

sentiment classification434

sequence data analysis319

sequences586

alignment590

biological586, 590–591

classification of589–590

similarity searches587

symbolic586, 588–590

time-series586, 587–588

sequential covering algorithm359

general-to-specific search360

greedy search361

illustrated359

rule induction with359–361

sequential pattern mining589

constraint-based589

in symbolic sequences588–589

shapelets method590

shared dimensions204

pruning205

shared-sorts193

shared-partitions193

shell cubes160

shell fragments192, 235

approach211–212

computation algorithm212, 213

computation example214–215

precomputing210

shrinking diameter592

sigmoid function402

signature-based detection614

significance levels373

significance measure312

significance tests372–373, 386

silhouette coefficient489–490

similarity

asymmetric binary71

cosine77–78

measuring65–78, 79

nominal attributes70

similarity measures447–448, 525–528

constraints on533

geodesic distance525–526

SimRank526–528

similarity searches587

in information networks594

in multimedia data mining596

simple random sample with replacement (SRSWR)108

simple random sample without replacement (SRSWOR)108

SimRank526–528, 539

computation527–528

random walk526–528

structural context528

simultaneous aggregation195

single-dimensional association rules17, 287

single-linkage algorithm460, 461

singular value decomposition (SVD)587

skewed data

balanced271

negatively47

positively47

wavelet transforms on102

slice operation148

small-world phenomenon592

smoothing112

by bin boundaries89

by bin means89

by bin medians89

for data discretization90

snowflake schema140

example141

illustrated141

star schema versus140

social networks524–525, 526–528

densification power law592

evolution of594

mining623

small-world phenomenon592 see alsonetworks

social science/social studies data mining613

soft clustering501

soft constraints534, 539

example534

handling536–537

space-filling curve58

sparse data102

sparse data cubes190

sparsest cuts539

sparsity coefficient579

spatial data14

spatial data mining595

spatiotemporal data analysis319

spatiotemporal data mining595, 623–624

specialized SQL servers165

specificity measure367

spectral clustering520–522, 539

effectiveness522

framework521

steps520–522

speech recognition430

speed, classification369

spiral method152

split-point333, 340, 342

splitting attributes333

splitting criterion333, 342

splitting rules. seeattribute selection measures

splitting subset333

SQL, as relational query language10

square-error function454

squashing function403

standard deviation51

example51

function of50

star schema139

example139–140

illustrated140

snowflake schema versus140

Star-Cubing204–210, 235

algorithm illustration209

bottom-up computation205

example207

for full cube computation210

ordering of dimensions and210

performance210

shared dimensions204–205

starnet query model149

example149–150

star-nodes205

star-trees205

compressed base table207

construction205

statistical data mining598–600

analysis of variance600

discriminant analysis600

factor analysis600

generalized linear models599–600

mixed-effect