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TEAMFLY
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Data Mining Techniques For Marketin
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To Stephanie, Sasha, and Nathaniel.
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xx Acknowledgments And, of course,
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TEAMFLY Team-Fly ®
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xxiv Introduction Even if the techn
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Contents Acknowledgments About the
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Contents vii Learning Things That A
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Contents ix Different Kinds of Chur
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Contents xi Chapter 8 How Does a Ne
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Contents xiii Case Study: Who Is Us
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Contents xv Chapter 14 Data Mining
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Contents xvii Availability of Train
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CHAPTER 1 Why and What Is Data Mini
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Why and What Is Data Mining? 3 In t
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Why and What Is Data Mining? 5 many
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Why and What Is Data Mining? 7 DATA
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Why and What Is Data Mining? 9 Clas
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Why and What Is Data Mining? 11 cho
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Why and What Is Data Mining? 13 man
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Why and What Is Data Mining? 15 Com
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Why and What Is Data Mining? 17 sit
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Why and What Is Data Mining? 19 And
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22 Chapter 2 Data is at the heart o
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24 Chapter 2 Marketing literature f
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26 Chapter 2 What Is the Virtuous C
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28 Chapter 2 that lurking inside th
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30 Chapter 2 possible to identify t
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32 Chapter 2 All of these measureme
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34 Chapter 2 Data mining results ch
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36 Chapter 2 Quota Savings Randomiz
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38 Chapter 2 Some of these fields r
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40 Chapter 2 How Data Mining Was Ap
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42 Chapter 2 smaller group of likel
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44 Chapter 3 years, the authors hav
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46 Chapter 3 Ford is the only one w
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48 Chapter 3 Figure 3.2 shows anoth
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50 Chapter 3 The data mining method
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52 Chapter 3 In the most general se
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54 Chapter 3 of maleness. It seems
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56 Chapter 3 Step One: Translate th
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58 Chapter 3 ■■ ■■ ■■ C
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60 Chapter 3 Data mining is often p
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62 Chapter 3 These operational syst
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64 Chapter 3 Often, variables that
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66 Chapter 3 90% 80% 70% 60% 50% 40
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68 Chapter 3 advantage as smarter p
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70 Chapter 3 Including Multiple Tim
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72 Chapter 3 People often find it h
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74 Chapter 3 When missing values mu
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76 Chapter 3 category, such as bake
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78 Chapter 3 Step Eight: Assess Mod
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80 Chapter 3 Percent of Row Frequen
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82 Chapter 3 An example helps to ex
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84 Chapter 3 Lift Value 1.5 1.4 1.3
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86 Chapter 3 before. The newly disc
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88 Chapter 4 comes from traditional
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90 Chapter 4 based on price will no
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92 Chapter 4 The problem with this
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94 Chapter 4 DATA BY CENSUS TRACT T
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96 Chapter 4 Actually, the first le
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98 Chapter 4 ROC CURVES Models are
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100 Chapter 4 The upper, curved lin
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102 Chapter 4 BENEFIT (continued) A
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104 Chapter 4 A smaller, better-tar
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106 Chapter 4 Reaching the People M
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108 Chapter 4 Difference in respons
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110 Chapter 4 Among the most useful
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112 Chapter 4 More typically, a bus
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114 Chapter 4 Nonrepayment of debt
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116 Chapter 4 Making Recommendation
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118 Chapter 4 Retention campaigns c
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120 Chapter 4 information than simp
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122 Chapter 4 From a data mining pe
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124 Chapter 5 What is remarkable an
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126 Chapter 5 TIP The simplest expl
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128 Chapter 5 Time Series Histogram
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130 Chapter 5 The Central Limit The
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132 Chapter 5 A QUESTION OF TERMINO
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134 Chapter 5 small probability. Pr
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136 Chapter 5 Cross-Tabulations Tim
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138 Chapter 5 In addition, various
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140 Chapter 5 the challenger offer.
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142 Chapter 5 Table 5.2 The 95 Perc
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144 Chapter 5 Table 5.3 The 95 Perc
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146 Chapter 5 What the Confidence I
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148 Chapter 5 says that with contro
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150 Chapter 5 The appeal of the chi
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152 Chapter 5 distribution depends
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154 Chapter 5 Table 5.7 Chi-Square
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156 Chapter 5 Table 5.8 Chi-Square
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158 Chapter 5 100% 80% 60% 40% 20%
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160 Chapter 5 There Is a Lot of Dat
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162 Chapter 5 Figure 5.11 shows ano
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CHAPTER 6 Decision Trees Decision t
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Decision Trees 167 thinks of a part
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Decision Trees 169 Scoring Figure 6
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Decision Trees 171 50% tot units de
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Decision Trees 173 The first split
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Decision Trees 175 the best splits,
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Decision Trees 177 Purity and Diver
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Decision Trees 179 Entropy Reductio
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Decision Trees 181 COMPARING TWO SP
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Decision Trees 183 statistical rela
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Decision Trees 185 The CART Pruning
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Decision Trees 187 COMPARING MISCLA
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Decision Trees 189 Picking the Best
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Decision Trees 191 The trees grown
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Decision Trees 193 WARNING Small no
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Decision Trees 195 Taking Cost into
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Decision Trees 197 Voter #1 and Vot
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Decision Trees 199 Neural Trees One
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Decision Trees 201 part of the targ
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Decision Trees 203 Decision Trees i
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Decision Trees 205 Applying Decisio
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Decision Trees 207 USING DECISION T
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Decision Trees 209 enjoyed using th
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212 Chapter 7 probing neural networ
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214 Chapter 7 of the value of the p
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216 Chapter 7 Table 7.1 Common Feat
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218 Chapter 7 Year_Built (1923), su
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220 Chapter 7 The solution is to in
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222 Chapter 7 Feed-forward networks
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224 Chapter 7 magnitude of the weig
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226 Chapter 7 Feed-Forward Neural N
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228 Chapter 7 last purchase age gen
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230 Chapter 7 TRAINING AS OPTIMIZAT
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232 Chapter 7 networks now takes se
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234 Chapter 7 Size of Training Set
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236 Chapter 7 This transformation (
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238 Chapter 7 Features with Ordered
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240 Chapter 7 be mapped to -1.0, -0
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242 Chapter 7 pattern the network f
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244 Chapter 7 1.0 B B B B A A B 0.0
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246 Chapter 7 Notice that the time-
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248 Chapter 7 2. Measure the output
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250 Chapter 7 The output units comp
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252 Chapter 7 unknown instance is f
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254 Chapter 7 The story continues w
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CHAPTER 8 Nearest Neighbor Approach
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Memory-Based Reasoning and Collabor
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Memory-Based Reasoning and Collabor
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Memory-Based Reasoning and Collabor
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Memory-Based Reasoning and Collabor
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Memory-Based Reasoning and Collabor
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Memory-Based Reasoning and Collabor
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Memory-Based Reasoning and Collabor
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Memory-Based Reasoning and Collabor
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Memory-Based Reasoning and Collabor
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Memory-Based Reasoning and Collabor
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Memory-Based Reasoning and Collabor
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Memory-Based Reasoning and Collabor
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Memory-Based Reasoning and Collabor
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Memory-Based Reasoning and Collabor
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338 Chapter 10 353 3658 00:00:41
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169 44 61 340 Chapter 10 The proces
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342 Chapter 10 USING SQL TO COLOR A
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5 MOU 344 Chapter 10 customer behav
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346 Chapter 10 Second, link analysi
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CHAPTER 11 Automatic Cluster Detect
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Automatic Cluster Detection 351 the
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Automatic Cluster Detection 353 The
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Automatic Cluster Detection 355 the
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Automatic Cluster Detection 357 X 2
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Automatic Cluster Detection 359 thi
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Automatic Cluster Detection 361 DIS
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Automatic Cluster Detection 363 Man
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Automatic Cluster Detection 365 Use
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Automatic Cluster Detection 367 The
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Automatic Cluster Detection 369 sub
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Automatic Cluster Detection 371 Dis
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Automatic Cluster Detection 373 is
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Automatic Cluster Detection 375 sig
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Automatic Cluster Detection 377 Cre
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Automatic Cluster Detection 379 Pop
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Automatic Cluster Detection 381 Les
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CHAPTER 12 Knowing When to Worry: H
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Hazard Functions and Survival Analy
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Hazard Functions and Survival Analy
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Hazard Functions and Survival Analy
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Hazard Functions and Survival Analy
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Hazard Functions and Survival Analy
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Hazard Functions and Survival Analy
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Hazard Functions and Survival Analy
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Hazard Functions and Survival Analy
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1 Hazard Functions and Survival Ana
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Hazard Functions and Survival Analy
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Hazard Functions and Survival Analy
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Hazard Functions and Survival Analy
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Hazard Functions and Survival Analy
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Hazard Functions and Survival Analy
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Hazard Functions and Survival Analy
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Hazard Functions and Survival Analy
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Hazard Functions and Survival Analy
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Hazard Functions and Survival Analy
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422 Chapter 13 problems involving c
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424 Chapter 13 template for the hum
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426 Chapter 13 generation n generat
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428 Chapter 13 SIMPLE OVERVIEW OF G
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430 Chapter 13 Table 13.3 The Popul
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432 Chapter 13 Table 13.5 The Popul
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434 Chapter 13 So far, this problem
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436 Chapter 13 schema match the cor
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438 Chapter 13 The Schema Theorem e
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440 Chapter 13 The first problem fa
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442 Chapter 13 trained to fill in a
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444 Chapter 13 Figure 13.7 The Gena
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446 Chapter 13 Lessons Learned Gene
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448 Chapter 14 has largely replaced
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450 Chapter 14 NO CUSTOMER RELATION
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452 Chapter 14 ■■ ■■ Automa
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454 Chapter 14 Such agent relations
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456 Chapter 14 Larger businesses, o
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458 Chapter 14 Subscription Relatio
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Respond from Some Channel Not Pay 4
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462 Chapter 14 Who Are the Prospect
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464 Chapter 14 What Is the Role of
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466 Chapter 14 New sales come in th
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468 Chapter 14 AN ENGINE FOR CHURN
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470 Chapter 14 Winback Once custome
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TEAMFLY Team-Fly ®
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474 Chapter 15 believe that, over t
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476 Chapter 15 The level of abstrac
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478 Chapter 15 effort. One of the g
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480 Chapter 15 WHAT IS A RELATIONAL
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482 Chapter 15 WHAT IS A RELATIONAL
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484 Chapter 15 warehouse must be re
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486 Chapter 15 One or more of these
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488 Chapter 15 Central Repository T
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490 Chapter 15 BACKGROUND ON PARALL
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492 Chapter 15 important type of da
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494 Chapter 15 The data warehouse i
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496 Chapter 15 In the middle are of
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498 Chapter 15 Shop Date Product sh
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500 Chapter 15 The third type of cu
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502 Chapter 15 ranges of customer v
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504 Chapter 15 Conformed Dimensions
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506 Chapter 15 In diagrams, the dim
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508 Chapter 15 One of the problems
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510 Chapter 15 graph. Neural networ
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512 Chapter 15 A typical data wareh
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514 Chapter 16 A Customer-Centric O
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516 Chapter 16 data is not readily
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518 Chapter 16 Operational Data (bi
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520 Chapter 16 Collecting the Right
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522 Chapter 16 devising new product
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524 Chapter 16 direct mail decrease
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526 Chapter 16 A new data mining gr
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528 Chapter 16 Scoring is not compl
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530 Chapter 16 three major modules,
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532 Chapter 16 What is appealing ab
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534 Chapter 16 account future growt
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536 Chapter 16 Comprehensible Outpu
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538 Chapter 16 step is to create a
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540 Chapter 17 budget for buying ha
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542 Chapter 17 It is perhaps unfort
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544 Chapter 17 The distribution of
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546 Chapter 17 Before ignoring a co
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548 Chapter 17 Figure 17.4 Angoss K
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550 Chapter 17 ■■ True numeric
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552 Chapter 17 Dates and Times Date
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554 Chapter 17 Neural networks and
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556 Chapter 17 One of the most impo
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558 Chapter 17 Constructing the Cus
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560 Chapter 17 Identifying the Cust
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562 Chapter 17 business customers o
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564 Chapter 17 Making Progress Alth
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566 Chapter 17 Changes over Time Pe
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568 Chapter 17 DM TM WEB Credit Car
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570 Chapter 17 When the lookup tabl
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572 Chapter 17 Pivoting Regular Tim
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574 Chapter 17 Summarizing Transact
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576 Chapter 17 One method of calcul
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578 Chapter 17 TIP When many differ
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580 Chapter 17 Revolvers, Transacto
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582 Chapter 17 Table 17.5 Six Credi
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584 Chapter 17 Table 17.6 Potential
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586 Chapter 17 $2,000 $1,500 $1,000
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588 Chapter 17 120 Payment as Multi
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590 Chapter 17 The Dark Side of Dat
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592 Chapter 17 Dirty Data Dirty dat
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594 Chapter 17 and so on. However,
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596 Chapter 17 varies from tool to
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598 Chapter 18 Getting Started The
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600 Chapter 18 These are areas wher
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602 Chapter 18 proof-of-concept pro
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604 Chapter 18 Although the details
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606 Chapter 18 less likely to churn
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608 Chapter 18 from one record to a
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610 Chapter 18 are appropriate for
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612 Chapter 18 serial number and ph
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614 Chapter 18 plan allows. Since t
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616 Index analysis differential res
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618 Index auxiliary information, 56
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620 Index champion-challenger appro
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622 Index creative process, data mi
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624 Index data (continued) missing
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626 Index discrete outcomes, classi
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628 Index genetic algorithms case s
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630 Index intuition, data explorati
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632 Index memory-based reasoning (M
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634 Index new customer information
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636 Index proof-of-concept projects
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638 Index response, survey response
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640 Index SQL data, time series ana
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642 Index testing (continued) KS (K