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• Classic-Fiction

hierarchical visualization63

treemaps63, 65

Worlds-with-Worlds63, 64

high-dimensional data301

clustering447

data distribution of560

frequent pattern mining301–307

outlier detection in576–580, 582

row enumeration302

high-dimensional data clustering497, 508–522, 538, 553

biclustering512–519

dimensionality reduction methods510, 519–522

example508–509

problems, challenges, and methodologies508–510

subspace clustering methods509, 510–511 see alsocluster analysis

HilOut algorithm577–578

histograms54, 106–108, 116

analysis by discretization115–116

attributes106

binning106

construction559

equal-frequency107

equal-width107

example54

illustrated55, 107

multidimensional108

as nonparametric model559

outlier detection using558–560

holdout method370, 386

holistic measures145

homogeneous networks592

classification of593

clustering of593

Hopkins statistic484–485

horizontal data format259

hybrid OLAP (HOLAP)164–165, 179

hybrid-dimensional association rules288

I

IBM Intelligent Miner603, 606

iceberg condition191

iceberg cubes160, 179, 190, 235

BUC construction201

computation160, 193–194, 319

computation and storage210–211

computation with Star-Cubing algorithm204–210

materialization319

specification of190–191 see alsodata cubes

icon-based visualization60

Chernoff faces60–61

stick figure technique61–63 see alsodata visualization

ID3332, 385

greedy approach332

information gain336 see alsodecision tree induction

IF-THEN rules355–357

accuracy356

conflict resolution strategy356

coverage356

default rule357

extracting from decision tree357

form355

rule antecedent355

rule consequent355

rule ordering357

satisfied356

triggered356

illustrated149

image data analysis319

imbalance problem367

imbalance ratio (IR)270

skewness271

inconvertible constraints300

incremental data mining31

indexes

bitmapped join163

composite join162

Gini332, 341–343

inverted212, 213

indexing

bitmap160–161, 179

bitmapped join179

frequent pattern mining for319

join161–163, 179

OLAP160–163

inductive databases601

inferential statistics24

information age, moving toward1–2

information extraction systems430

information gain336–340

decision tree induction using338–339

ID3 use of336

pattern frequency support versus421

single feature plot420

split-point340

information networks

analysis592–593

evolution of594

link prediction in593–594

mining623

OLAP in594

role discovery in593–594

similarity search in594

information processing153

information retrieval (IR)26–27

challenges27

language model26

topic model26–27

informativeness model535

initial working relations168, 169, 177

instance-based learners. seelazy learners

instances, constraints on533, 539

integrated data warehouses126

integrators127

intelligent query answering618

interactive data mining604, 607

interactive mining30

intercuboid query expansion221

example224–225

method223–224

interdimensional association rules288

interestingness21–23

assessment methods23

components of21

expected22

objective measures21–22

strong association rules264–265

subjective measures22

threshold21–22

unexpected22

interestingness constraints294

application of297

interpretability

backpropagation and406–408

classification369

cluster analysis447

data85

data quality and85

probabilistic hierarchical clustering469

interquartile range (IQR)49, 555

interval-scaled attributes43, 79

intracuboid query expansion221

example223

method221–223

value usage222

intradimensional association rules287

intrusion detection569–570

anomaly-based614

data mining algorithms614–615

discriminative classifiers615

distributed data mining615

signature-based614

stream data analysis615

visualization and query tools615

inverted indexes212, 213

invisible data mining33, 618–620, 625

IQR. seeInterquartile range

IR. seeinformation retrieval

item merging263