© Copyright IBM Corp. 2001 193
Index
A
aggregation 34, 37, 55, 57, 66, 142
AIX 180
analysis
statistical 23
applications 28, 34
association
rules 154, 159
type 145, 148
B
basket analysis 54
BI 1
binary tree 107
bivariate statistics 39
business
definitions 45
issue 2, 29, 31, 99
reporting tools 23
rules 46, 49, 68
user 43
users 5
Business Intelligence 1, 179
C
campaign 46, 138
challenges 40, 137
CLA 100, 118, 131
classification 117
clustering
demographic 71
collaborative filtering 140, 153
Condorcet 72, 75
confidence 125, 145, 148
content filtering 139, 153
correlated 104
correlation 39, 40, 67, 146
CRM 2, 45, 92
cross-sell 137, 139, 158, 163
Customer Relationship Management 2, 45
customers 2, 45, 46
attributes 46
average spend per visit 25
behavior 39, 54
characteristics 49
distribution 115
distribution of spending 169
Family Shoppers 81
frequent visitor 46
grouping 46
groups 98
high spender 46
identifier 51, 54, 55
loyal 46
new potential 97
ranking 46
scoring 93
segmentation 28, 47
segments 45
shoppers 170
store own brand 46
customized 172
D
data
additional data 49
aggregation 11
categorical 70
clean up 173
cleansing 10, 34
content 35
customer relationship data 49
demographic data 35, 49
description 35
evaluating 29, 38, 63, 103, 144
extraction 9
filtering 174
historical 9
model 29, 34, 36, 49, 53, 92, 100, 142, 172
preparation 37, 173
preprocessing 36, 101, 108
product data 49
propagation 9
refining 10
relationship data 35
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
Index 195
J
join 39, 174
K
kiosk 134
L
lift 125, 145, 148
linear regression 39
Linux 180
Linux/390 180
lower-case 173
loyalty card 51, 56
M
mapping tables 10
market
basket analysis 28, 40, 147
segments 97
marketing analyst 43
marketing campaign 131
metadata 12, 14, 34
business 14
formal 14
informal 14
sources 14
technical 13
models 101
multidimensional view 19
N
niche 111, 172
Normalized Relative Spend 54, 141
NRS 54, 152
NULL 173
O
OLAP
applications 18
calculation 18
systems 18
OLTP 179
operational data source 15
ORACLE 41, 92, 173, 181
P
parallel 172
patterns 27, 32, 40
PDA 167, 169
performance 113, 118, 124, 126, 128
Personal Digital Assistants 167
pivot 174
PMML 42, 92, 181
Point-of-Sale 50, 134
Point-of-Sale Transaction Data 56
polynomial regression 39
POS 56
Predictive Model Markup Language 42, 92
preprocessing 56
products 2, 46, 138
appeal 137
combinations 140
hierarchy 56, 156
identifying 140
personalized 144
placement 137
recommendations 138, 161, 170
scoring 140
taxonomy 54, 143
type 146
project
owner 43
propagation 9
pruning 107
pull 10
purchasers 150
push 10
Q
quantile 116, 128, 173
R
ranges 174
rank 132
RBF 104, 111, 179
Redbooks Web site 184
Contact us x
Relational Connect feature 172
relational database 24, 173
relationship 26, 32, 35, 146
relative spend 55
reliable results 24
repository 13
196 Mining Your Own Business in Retail Using DB2 Intelligent Miner for Data
resolution of errors 39
results 67
biased 39
deploying 30, 41, 92, 131, 158, 167
how to read them 79
interpreting 30, 41, 79, 118, 162
visualizing 179
retail 45
outlet 2
retailer 137
Return On Investment 131
revenue 132
rewards 51
risk analysis 28
RMS 115
roadmap 13
Root Mean Square error 115
S
sales and marketing 46
sales ticket 50
sample 102, 111
sampling 102, 111
SAP 172
scoring 92, 133, 134, 156, 180
segmentation 98
segments 68, 98
shoppers
affluent 58
alcohol 58
family 58
general 58
hobby 58
out of town 25
similar characteristics 46
similarity 72, 75, 112
threshold 72
skills 42
solution 24, 140
sourcing 141
split 105, 113
SQL 174
SQLServer 173
statistical 39
statistics 104
stores 2, 50
inner-city 25
summarize 10, 174
support 145, 148
SYBASE 173
T
table 173
copy 173
view 35, 37
taxonomies 172
team 42
technique 23
TERADATA 173
threshold 117
time intelligence 20
TLA 100, 102, 118, 121
tools 23
transaction
date 50, 54
number 54
U
Universal Product Code 50
UPC 50, 54
UPI 142
upper-case 173
up-sell 137
V
values
aggregate 173
calculate 173
chi-square 62, 69, 80
continuous 174
map 174
missing 39, 65, 173
non-valid 174
outlying 39, 65
variables 36, 39
correlation 67
dependent 39
selection 39, 66, 108
view 57
visual inspections 39
visualization 63, 113, 116
visualizers 163, 172
W
weight 110, 115
Index 197
Windows 2000 180
Windows NT 180

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