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• Classic-Fiction

microeconomic view601

midrange47

MineSet603, 605

minimal interval size116

minimal spanning tree algorithm462

minimum confidence threshold18, 245

Minimum Description Length (MDL)343–344

minimum support threshold18, 190

association rules245

count246

Minkowski distance73

min-max normalization114

missing values88–89

mixed-effect models600

mixture models503, 538

EM algorithm for507–508

univariate Gaussian504

mode39, 47

example47

model selection364

with statistical tests of significance372–373

models18

modularity

of clustering530

use of539

MOLAP. seemultidimensional OLAP

monotonic constraints298

motifs587

moving-object data mining595–596, 623–624

multiclass classification430–432, 437

all-versus-all (AVA)430–431

error-correcting codes431–432

one-versus-all (OVA)430

multidimensional association rules17, 283, 288, 320

hybrid-dimensional288

interdimensional288

mining287–289

mining with static discretization of quantitative attributes288

with no repeated predicates288 see alsoassociation rules

multidimensional data analysis

in cube space227–234

in multimedia data mining596

spatial595

of top-k results226

multidimensional data mining11–13, 34, 155–156, 179, 187, 227, 235

data cube promotion of26

dimensions33

example228–229

retail industry610

multidimensional data model135–146, 178

data cube as136–139

dimension table136

dimensions142–144

fact constellation141–142

fact table136

snowflake schema140–141

star schema139–140

multidimensional databases

measures of146

querying with starnet model149–150

multidimensional histograms108

multidimensional OLAP (MOLAP)132, 164, 179

multifeature cubes227, 230, 235

complex query support231

examples230–231

multilayer feed-forward neural networks398–399

example405

illustrated399

layers399

units399

multilevel association rules281, 283, 284, 320

ancestors287

concept hierarchies285

dimensions281

group-based support286

mining283–287

reduced support285, 286

redundancy, checking287

uniform support285–286

multimedia data14

multimedia data analysis319

multimedia data mining596

multimodal47

multiple linear regression90, 106

multiple sequence alignment590

multiple-phase clustering458–459

multitier data warehouses134

multivariate outlier detection556

with Mahalanobis distance556

with multiple clusters557

with multiple parametric distributions557

with χ2-static556

multiway array aggregation195, 235

for full cube computation195–199

minimum memory requirements198

must-link constraints533, 536

mutation operator426

mutual information315–316

mutually exclusive rules358

N

naive Bayesian classification385

class label prediction with353–355

functioning of351–352

nearest-neighbor clustering algorithm461

near-match patterns/rules281

negative correlation55, 56

negative patterns280, 283, 320

example291–292

mining291–294

negative transfer436

negative tuples364

negatively skewed data47

neighborhoods

density471

distance-based outlier detection560

k-distance565

nested loop algorithm561, 562

networked data14

networks592

heterogeneous592, 593

homogeneous592, 593

information592–594

mining in science applications612–613

social592

statistical modeling of592–594

neural networks19, 398

backpropagation398–408

as black boxes406

for classification19, 398

disadvantages406

fully connected399, 406–407

learning398

multilayer feed-forward398–399

pruning406–407

rule extraction algorithms406, 407

sensitivity analysis408

three-layer399

topology definition400

two-layer399

neurodes399

Ng-Jordan-Weiss algorithm521, 522

no materialization159

noise filtering318

noisy data89–91

nominal attributes41

concept hierarchies for284

correlation analysis95–96

dissimilarity between69

example41

proximity measures68–70

similarity computation70

values of79, 288 see alsoattributes

nonlinear SVMs413–415

nonparametric statistical