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• Classic-Fiction

database types and32

descriptive15

distributed615, 624

efficiency31

foundations, views on600–601

functionalities15–23, 34

graphs and networks591–594

incremental31

as information technology evolution2–5

integration623

interactive30

as interdisciplinary effort29–30

invisible33, 618–620, 625

issues in29–33, 34

in knowledge discovery7

as knowledge search through data6

machine learning similarities26

methodologies29–30, 585–607

motivation for1–5

multidimensional11–13, 26, 33–34, 155–156, 179, 227–230

multimedia data596

OLAP and154

as pattern/knowledge discovery process8

predictive15

presentation/visualization of results31

privacy-preserving32, 621–622, 624–625, 626

query languages31

relational databases10

scalability31

sequence data586

social impacts32

society and618–622

spatial data595

spatiotemporal data and moving objects595–596, 623–624

statistical598

text data596–597, 624

trends622–625, 626

ubiquitous618–620, 625

user interaction and30–31

visual and audio602–607, 624, 625

Web data597–598, 624

data mining systems10

data models

entity-relationship (ER)9, 139

multidimensional135–146

data objects40, 79

similarity40

terminology for40

data preprocessing83–124

cleaning88–93

forms illustration87

integration93–99

overview84–87

quality84–85

reduction99–111

in science applications612

summary87

tasks in85–87

transformation111–119

data quality84, 120

accuracy84

believability85

completeness84–85

consistency85

interpretability85

timeliness85

data reduction86, 99–111, 120

attribute subset selection103–105

clustering108

compression100, 120

data cube aggregation110–111

dimensionality86, 99–100, 120

histograms106–108

numerosity86, 100, 120

parametric105–106

principle components analysis102–103

sampling108

strategies99–100

theory601

wavelet transforms100–102 see alsodata preprocessing

data rich but information poor5

data scrubbing tools92

data security-enhancing techniques621

data segmentation445

data selection8

data source view151

data streams14, 598, 624

data transformation8, 87, 111–119, 120

aggregation112

attribute construction112

in back-end tools/utilities134

concept hierarchy generation112, 120

discretization111, 112, 120

normalization112, 113–115, 120

smoothing112

strategies112–113 see alsodata preprocessing

data types

complex166

complex, mining585–598

for data mining8

data validation592–593

data visualization56–65, 79, 602–603

complex data and relations64–65

geometric projection techniques58–60

hierarchical techniques63–64

icon-based techniques60–63

mining process603

mining result603, 605

pixel-oriented techniques57–58

in science applications613

summary65

tag clouds64, 66

techniques39–40

data warehouses10–13, 26, 33, 125–185

analytical processing153

back-end tools/utilities134, 178

basic concepts125–135

bottom-up design approach133, 151–152

business analysis framework for150

business query view151

combined design approach152

data mart132, 142

data mining154

data source view151

design process151

development approach133

development tools153

dimensions10

enterprise132

extractors151

fact constellation141–142

for financial data608

framework illustration11

front-end client layer132

gateways131

geographic595

implementation156–165

information processing153

integrated126

metadata134–135

modeling10, 135–150

models132–134

multitier134

multitiered architecture130–132

nonvolatile127

OLAP server132

operational database systems versus128–129

planning and analysis tools153

retail industry609–610

in science applications612

snowflake schema140–141

star schema139–140

subject-oriented126

three-tier architecture131, 178

time-variant127

tools11

top-down design approach133, 151

top-down view151

update-driven approach128