Index R

data mining: opportunities and challenges
Index
Data Mining: Opportunities and Challenges
by John Wang (ed) 
Idea Group Publishing 2003
Brought to you by Team-Fly

S

sampling 227

scalability 224, 265

scalable data-mining solutions for freetext 281

scatter plots 62

schizophrenia 22

search engines 302

segmentation 227

self-organizing maps (SOM) 32, 199, 201, 325, 327

semantic Web 322

semi-structured documents 439

seq 112

sequential- pattern mining 445

shared memory 114

shared memory multiprocessors (SMP) 108

shared or integrated systems topologies 356

shared tree (ST) 127

shared-memory multiprocessors 107

similarity search 445

Simpson's paradox 13

simulation techniques 260

single driver single cost pool case 255

site modification 440

social network analysis 443

software engineering 422

software reuse 111

soil fertility mapping 33

spatial and geographic data 444

spatial clustering 108, 128

spatial data cubes 444

spatial OLAP 444

spatial queries 129

spatial warehouses 444

specialist-moderator (SM) 37

specificity 167

spider 304

statistical inference 2

statistical models 1

streams 112

strength 146

strength factor 167

structural organization of information 279

structured parallel languages 107

structured parallel version 106

subjectivity 265

summarization 231

supervised learning networks 312

support 146, 167

syntactic objects 280

system improvement 439

system transparency 109

Brought to you by Team-Fly


Data Mining(c) Opportunities and Challenges
Data Mining: Opportunities and Challenges
ISBN: 1591400511
EAN: 2147483647
Year: 2003
Pages: 194
Authors: John Wang

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