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• Classic-Fiction

in recommender systems319

road map279–283

scalable computation and319

scope of319–320

in sequence or structural data analysis319

in spatiotemporal data analysis319

for structure and cluster discovery318

for subspace clustering318–319

in time-series data analysis319

top-k310

in video data analysis319 see alsofrequent patterns

frequent pattern-based classification415–422, 437

associative415, 416–419

discriminative416, 419–422

framework422

frequent patterns17, 243

abstraction levels281

association rule mapping280

basic280

closed262–264, 280

concepts243–244

constraint-based281

dimensions281

diversity280

exploration313–319

growth257–259, 272

max262–264, 280

mining243–244, 279–325

mining constraints or criteria281

number of dimensions involved in281

semantic annotation of313–317

sequential243

strong associations437

structured243

trees257–259

types of values in281

frequent subgraphs591

front-end client layer132

full materialization159, 179, 234

fuzzy clustering499–501, 538

data set for506

with EM algorithm505–507

example500

expectation step (E-step)505

flexibility501

maximization step (M-step)506–507

partition matrix499

as soft clusters501

fuzzy logic428

fuzzy sets428–429, 437, 499

evaluation500–501

example499

G

gain ratio340

C4.5 use of340

formula341

maximum341

gateways131

gene expression513–514

generalization

attribute169–170

attribute, control170

attribute, threshold control170

in multimedia data mining596

process172

results presentation174

synchronous175

generalized linear models599–600

generalized relations

attribute-oriented induction172

presentation of174

threshold control170

generative model467–469

genetic algorithms426–427, 437

genomes15

geodesic distance525–526, 539

diameter525

eccentricity525

measurements based on526

peripheral vertex525

radius525

geographic data warehouses595

geometric projection visualization58–60

Gini index341

binary enforcement332

binary indexes341

CART use of341

decision tree induction using342–343

minimum342

partitioning and342

global constants, for missing values88

global outliers545, 581

detection545

example545

Google

Flu Trends2

popularity of619–620

gradient descent strategy396–397

algorithms397

greedy hill-climbing397

as iterative396–397

graph and network data clustering497, 522–532, 539

applications523–525

bipartite graph523

challenges523–525, 530

cuts and clusters529–530

generic method530–531

geodesic distance525–526

methods528–532

similarity measures525–528

SimRank526–528

social network524–525

web search engines523–524 see alsocluster analysis

graph cuts539

graph data14

graph index structures591

graph pattern mining591–592, 612–613

graphic displays

data presentation software44–45

histogram54, 55

quantile plot51–52

quantile-quantile plot52–54

scatter plot54–56

greedy hill-climbing397

greedy methods, attribute subset selection104–105

grid-based methods450, 479–483, 491

CLIQUE481–483

STING479–481 see alsocluster analysis

grid-based outlier detection562–564

CELL method562, 563

cell properties562

cell pruning rules563 see alsooutlier detection

group-based support286

group-by clause231

grouping attributes231

grouping variables231

Grubb' test555

H

hamming distance431

hard constraints534, 539

example534

handling535–536

harmonic mean369

hash-based technique255

heterogeneous networks592

classification of593

clustering of593

ranking of593

heterogeneous transfer learning436

hidden Markov model (HMM)590, 591

hierarchical methods449, 457–470, 491

agglomerative459–461

algorithmic459, 461–462

Bayesian459

BIRCH458, 462–466

Chameleon458, 466–467

complete linkages462, 463

distance measures461–462

divisive459–461

drawbacks449

merge or split points and458

probabilistic459, 467–470

single linkages462, 463 see alsocluster analysis