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• Classic-Fiction

item skipping263

items13

itemsets246

candidate251, 252

dependent266

dynamic counting256

imbalance ratio (IR)270, 271

negatively correlated292

occurrence independence266

strongly negatively correlated292 see alsofrequent itemsets

iterative Pattern-Fusion306

iterative relocation techniques448

J

Jaccard coefficient71

join indexing161–163, 179

K

k-anonymity method621–622

Karush-Kuhn-Tucker (KKT) conditions412

k-distance neighborhoods565

kernel density estimation477–478

kernel function415

k-fold cross-validation370–371

k-means451–454

algorithm452

application of454

CLARANS457

within-cluster variation451, 452

clustering by453

drawback of454–455

functioning of452

scalability454

time complexity453

variants453–454

k-means clustering536

k-medoids454–457

absolute-error criterion455

cost function for456

PAM455–457

k-nearest-neighbor classification423

closeness423

distance-based comparisons425

editing method425

missing values and424

number of neighbors424–425

partial distance method425

speed425

knowledge

background30–31

mining29

presentation8

representation33

transfer434

knowledge bases5, 8

knowledge discovery

data mining in7

process8

knowledge discovery from data (KDD)6

knowledge extraction. seedata mining

knowledge mining. seedata mining

knowledge type constraints294

k-predicate sets289

Kulczynski measure268, 272

negatively correlated pattern based on293–294

L

language model26

Laplacian correction355

lattice of cuboids139, 156, 179, 188–189, 234

lazy learners393, 422–426, 437

case-based reasoning classifiers425–426

k-nearest-neighbor classifiers423–425

l-diversity method622

learning

active430, 433–434, 437

backpropagation400

as classification step328

connectionist398

by examples445

by observation445

rate397

semi-supervised572

supervised330

transfer430, 434–436, 438

unsupervised330, 445, 490

learning rates403–404

leave-one-out371

lift266, 272

correlation analysis with266–267

likelihood ratio statistic363

linear regression90, 105

multiple106

linearly412–413

linearly inseparable data413–415

link mining594

link prediction594

load, in back-end tools/utilities134

loan payment prediction608–609

local outlier factor566–567

local proximity-based outliers564–565

logistic function402

log-linear models106

lossless compression100

lossy compression100

lower approximation427

M

machine learning24–26

active25

data mining similarities26

semi-supervised25

supervised24

unsupervised25

Mahalanobis distance556

majority voting335

Manhattan distance72–73

MaPle519

margin410

market basket analysis244–246, 271–272

example244

illustrated244

Markov chains591

materialization

full159, 179, 234

iceberg cubes319

no159

partial159–160, 192, 234

semi-offline226

max patterns280

max_confidence measure268, 272

maximal frequent itemsets247, 308

example248

mining262–264

shortcomings for compression308–309

maximum marginal hyperplane (MMH)409

SVM finding412

maximum normed residual test555

mean39, 45

bin, smoothing by89

example45

for missing values88

trimmed46

weighted arithmetic45

measures145

accuracy-based369

algebraic145

all_confidence272

antimonotonic194

attribute selection331

categories of145

of central tendency39, 44, 45–47

correlation266

data cube145

dispersion48–51

distance72–74, 461–462

distributive145

holistic145

Kulczynski272

max_confidence272

of multidimensional databases146

null-invariant272

pattern evaluation267–271

precision368–369

proximity67, 68–72

recall368–369

sensitivity367

significance312

similarity/dissimilarity65–78

specificity367

median39, 46

bin, smoothing by89

example46

formula46–47

for missing values88

metadata92, 134, 178

business135

importance135

operational135

repositories134–135

metarule-guided mining

of association rules295–296

example295–296

metrics73

classification