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• Classic-Fiction

base111, 137–138, 158

child193

individual190

lattice of139, 156, 179, 188–189, 234, 290

sparse190

subset selection160 see alsodata cubes

curse of dimensionality158, 179

customer relationship management (CRM)619

customer retention analysis610

CVQE. seeConstrained Vector Quantization Error algorithm

cyber-physical systems (CPS)596, 623–624

D

data

antimonotonicity300

archeology6

biological sequence586, 590–591

complexity32

conversion to knowledge2

cyber-physical system596

for data mining8

data warehouse13–15

database9–10

discrimination16

dredging6

generalizing150

graph14

growth2

linearly inseparable413–415

linearly separated409

multimedia14, 596

multiple sources15, 32

multivariate556

networked14

overfitting330

relational10

sample219

similarity and dissimilarity measures65–78

skewed47, 271

spatial14, 595

spatiotemporal595–596

specializing150

statistical descriptions44–56

streams598

symbolic sequence586, 588–589

temporal14

text14, 596–597

time-series586, 587

“tombs”5

training18

transactional13–14

types of33

web597–598

data auditing tools92

data characterization15, 166

attribute-oriented induction167–172

data mining query167–168

example16

methods16

output16

data classification. seeclassification

data cleaning6, 85, 88–93, 120

in back-end tools/utilities134

binning89–90

discrepancy detection91–93

by information network analysis592–593

missing values88–89

noisy data89

outlier analysis90

pattern mining for318

as process91–93

regression90 see alsodata preprocessing

data constraints294

antimonotonic300

pruning data space with300–301

succinct300 see alsoconstraints

data cube aggregation110–111

data cube computation156–160, 214–215

aggregation and193

average()215

BUC200–204, 235

cube operator157–159

cube shells211

full189–190, 195–199

general strategies for192–194

iceberg160, 193–194

memory allocation199

methods194–218, 235

multiway array aggregation195–199

one-pass198

preliminary concepts188–194

shell fragments210–218, 235

Star-Cubing204–210, 235

data cubes10, 136, 178, 188

3-D138

4-D138, 139

apex cuboid111, 138, 158

base cuboid111, 137–138, 158

closed192

cube shell192

cuboids137

curse of dimensionality158

discovery-driven exploration231–234

example11–13

full189–190, 196–197

gradient analysis321

iceberg160, 190–191, 201, 235

lattice of cuboids157, 234, 290

materialization159–160, 179, 234

measures145

multidimensional12, 136–139

multidimensional data mining and26

multifeature227, 230–231, 235

multimedia596

prediction227–230, 235

qualitative association mining289–290

queries230

query processing218–227

ranking225–227, 235

sampling218–220, 235

shell160, 211

shell fragments192, 210–218, 235

sparse190

spatial595

technology187–242

data discretization. seediscretization

data dispersion44, 48–51

boxplots49–50

five-number summary49

quartiles48–49

standard deviation50–51

variance50–51

data extraction, in back-end tools/utilities134

data focusing168

data generalization179–180

by attribute-oriented induction166–178

data integration6, 85–86, 93–99, 120

correlation analysis94–98

detection/resolution of data value conflicts99

entity identification problem94

by information network analysis592–593

object matching94

redundancy and94–98

schema94

tuple duplication98–99 see alsodata preprocessing

data marts132, 142

data warehouses versus142

dependent132

distributed134

implementation132

independent132

data matrix67–68

dissimilarity matrix versus67–68

relational table67–68

rows and columns68

as two-mode matrix68

data migration tools93

data mining5–8, 33, 598, 623

ad hoc31

applications607–618

biological data624

complex data types585–598, 625

cyber-physical system data596

data streams598

data types for8

data warehouses for154