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• Classic-Fiction

nonvolatile data warehouses127

normalization112, 120

data transformation by113–115

by decimal scaling115

min-max114

z-score114–115

null rules92

null-invariant measures270–271, 272

null-transactions270, 272

number of270

problem292–293

numeric attributes43–44, 79

covariance analysis98

interval-scaled43, 79

ratio-scaled43–44, 79

numeric data, dissimilarity on72–74

numeric prediction328, 385

classification328

support vector machines (SVMs) for408

numerosity reduction86, 100, 120

techniques100

O

object matching94

objective interestingness measures21–22

one-class model571–572

one-pass cube computation198

one-versus-all (OVA)430

online analytical mining (OLAM)155, 227

online analytical processing (OLAP)4, 33, 128, 179

access patterns129

data contents128

database design129

dice operation148

drill-across operation148

drill-down operation11, 135–136, 146

drill-through operation148

example operations147

functionalities of154

hybrid OLAP164–165, 179

indexing125, 160–163

in information networks594

in knowledge discovery process125

market orientation128

multidimensional (MOLAP)132, 164, 179

OLTP versus128–129, 130

operation integration125

operations146–148

pivot (rotate) operation148

queries129, 130, 163–164

query processing125, 163–164

relational OLAP132, 164, 165, 179

roll-up operation11, 135–136, 146

sample data effectiveness219

server architectures164–165

servers132

slice operation148

spatial595

statistical databases versus148–149

user-control versus automation167

view129

online transaction processing (OLTP)128

access patterns129

customer orientation128

data contents128

database design129

OLAP versus128–129, 130

view129

operational metadata135

OPTICS473–476

cluster ordering474–475, 477

core-distance475

density estimation477

reachability-distance475

structure476

terminology476 see alsocluster analysis; density-based methods

ordered attributes103

ordering

class-based358

dimensions210

rule357

ordinal attributes42, 79

dissimilarity between75

example42

proximity measures74–75

outlier analysis20–21

clustering-based techniques66

example21

in noisy data90

spatial595

outlier detection543–584

angle-based (ABOD)580

application-specific548–549

categories of581

CELL method562–563

challenges548–549

clustering analysis and543

clustering for445

clustering-based methods552–553, 560–567

collective548, 575–576

contextual546–547, 573–575

distance-based561–562

extending577–578

global545

handling noise in549

in high-dimensional data576–580, 582

with histograms558–560

intrusion detection569–570

methods549–553

mixture of parametric distributions556–558

multivariate556

novelty detection relationship545

proximity-based methods552, 560–567, 581

semi-supervised methods551

statistical methods552, 553–560, 581

supervised methods549–550

understandability549

univariate554

unsupervised methods550

outlier subgraphs576

outliers

angle-based20, 543, 544, 580

collective547–548, 581

contextual545–547, 573, 581

density-based564

distance-based561

example544

global545, 581

high-dimensional, modeling579–580

identifying49

interpretation of577

local proximity-based564–565

modeling548

in small clusters571

types of545–548, 581

visualization with boxplot555

oversampling384, 386

example384–385

P

pairwise alignment590

pairwise comparison372

PAM. seePartitioning Around Medoids algorithm

parallel and distributed data-intensive mining algorithms31

parallel coordinates59, 62

parametric data reduction105–106

parametric statistical methods553–558

Pareto distribution592

partial distance method425

partial materialization159–160, 179, 234

strategies192

partition matrix538

partitioning

algorithms451–457

in Apriori efficiency255–256

bootstrapping371, 386

criteria447

cross-validation370–371, 386

Gini