194 Mining Your Own Business in Retail Using DB2 Intelligent Miner for Data
sources 35, 36
sourcing 29, 56, 101
sourcing and preprocessing 36
summarization 11
transaction 54, 142
transactional 35, 50
transactional data 35, 49
transformation 10
type 35
usage 35
volumes 24, 34
data engineering team 43
Data Mining Group 181
data mining techniques
associations 3, 40, 137, 141, 144
associations discovery 172
choosing 30, 40, 69, 104, 144
classification 3, 28, 40, 98, 99, 172
clustering 3, 27, 40, 47, 70, 172
decision tree 28, 112, 117
demographic clustering 70, 71
discovery 27, 98
frequency analysis 28
linear regression 28
link analysis 27
neural clustering 70, 71
neural networks 104, 179
neural prediction 28
polynomial regression 28
prediction 27, 173
Principal Component Analysis 110
Radial Basis Functions 28, 104, 179
RBF 28
sequential patterns 172
similar patterns 40
similar time sequences 40, 173, 179
tree classification 180
value prediction 28, 40
data set
test 100, 101, 104
training 101, 104, 105
data sources
data warehouse 9
operational 15
data warehouse 34, 53
architecture 8
database
view 37
datamart 11, 34, 37
DB2 41, 92
DB2 Intelligent Miner Scoring 42, 171, 179
decision makers 1
deviation 104
direct mailing 131
discretize 173
dissimilarity 72
distributions 39
E
eCommerce 179
error weighting 110
external data 9
extraction 9
F
flat files 173
fraud detection 28
G
gains charts 124, 126, 128, 129, 131
generic method 5, 23, 26
GINI 105
GUI 174, 181
guide 5, 23
guideline 5
H
heuristics 155
alternative 155
rules 153
historical data 9
hypotheses 25
I
IBM DataJoiner 172, 173
IBM DB2 Intelligent Miner for Data 171
IBM DB2 Universal Database 172, 173
IM for Data vii
IM Scoring 134, 179
implementers 5
inconsistencies 39, 65
INFORMIX 173
intervals 174
IT analyst 43
item code 50
Item purchase 142