- Page 1 and 2: TEAMFLY
- Page 6: Data Mining Techniques For Marketin
- Page 10: To Stephanie, Sasha, and Nathaniel.
- Page 16: xx Acknowledgments And, of course,
- Page 20: TEAMFLY Team-Fly ®
- Page 24: xxiv Introduction Even if the techn
- Page 30: Contents Acknowledgments About the
- Page 34: Contents vii Learning Things That A
- Page 38: Contents ix Different Kinds of Chur
- Page 42: Contents xi Chapter 8 How Does a Ne
- Page 46: Contents xiii Case Study: Who Is Us
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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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CHAPTER 9 Market Basket Analysis an
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Market Basket Analysis and Associat
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Market Basket Analysis and Associat
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Market Basket Analysis and Associat
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Market Basket Analysis and Associat
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Market Basket Analysis and Associat
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Market Basket Analysis and Associat
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Market Basket Analysis and Associat
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Market Basket Analysis and Associat
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Market Basket Analysis and Associat
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Market Basket Analysis and Associat
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Market Basket Analysis and Associat
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Market Basket Analysis and Associat
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Market Basket Analysis and Associat
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Market Basket Analysis and Associat
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Market Basket Analysis and Associat
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Market Basket Analysis and Associat
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CHAPTER 10 Link Analysis The intern
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Link Analysis 323 four people, all
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Link Analysis 325 Bananas Red Leaf
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Link Analysis 327 WHY DO THE DEGREE
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Link Analysis 329 This lack of scal
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Link Analysis 331 cannot be part of
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Link Analysis 333 a link to Harvard
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Link Analysis 335 Hubs Authorities
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Link Analysis 337 There are many ap
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Link Analysis 339 is good for guida
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Link Analysis 341 Figure 10.10 show
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Link Analysis 343 Case Study: Segme
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Link Analysis 345 Jane also racks u
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Link Analysis 347 Although link ana
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350 Chapter 11 autumn, typically to
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352 Chapter 11 Two different astron
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354 Chapter 11 Unlike the tradition
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356 Chapter 11 X 2 X 1 Figure 11.4
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358 Chapter 11 Figure 11.6 These ex
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360 Chapter 11 True measures are in
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362 Chapter 11 The angle between ve
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364 Chapter 11 But what if X is mea
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366 Chapter 11 Gaussian mixture mod
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368 Chapter 11 Agglomerative Cluste
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370 Chapter 11 Clusters and Trees T
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372 Chapter 11 algorithm is to supp
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374 Chapter 11 Case Study: Clusteri
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376 Chapter 11 Each of the scores o
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378 Chapter 11 N W E S 0 2.5 5 mile
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380 Chapter 11 Using Thematic Clust
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TEAMFLY Team-Fly ®
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384 Chapter 12 of loyalty—that th
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386 Chapter 12 may be one-time only
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388 Chapter 12 100% 90% 80% Percent
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390 Chapter 12 100% 90% 80% Percent
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392 Chapter 12 PARAMETRIC APPROACHE
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394 Chapter 12 Hazards The precedin
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396 Chapter 12 The same idea can be
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398 Chapter 12 When the contract is
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400 Chapter 12 time Figure 12.7 In
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402 Chapter 12 Table 12.4 From Time
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404 Chapter 12 These two customers
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406 Chapter 12 At any point in time
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408 Chapter 12 A NOTE ABOUT SURVIVA
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410 Chapter 12 Stratification: Meas
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412 Chapter 12 The biggest assumpti
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Hazard Probability ("Risk" of React
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416 Chapter 12 Number Actual Predic
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418 Chapter 12 100% 90% 80% 70% Sur
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CHAPTER 13 Genetic Algorithms Like
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Genetic Algorithms 423 The first wo
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Genetic Algorithms 425 GAs work by
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Genetic Algorithms 427 SIMPLE OVERV
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Genetic Algorithms 429 Selection Th
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Genetic Algorithms 431 Table 13.4 T
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Genetic Algorithms 433 the genome.
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Genetic Algorithms 435 which were v
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Genetic Algorithms 437 010 011 01*
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Genetic Algorithms 439 Application
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Genetic Algorithms 441 ■■ ■
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Genetic Algorithms 443 The comment
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Genetic Algorithms 445 easily confu
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CHAPTER 14 Data Mining throughout t
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Data Mining throughout the Customer
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Data Mining throughout the Customer
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Data Mining throughout the Customer
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Data Mining throughout the Customer
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Data Mining throughout the Customer
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Data Mining throughout the Customer
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Data Mining throughout the Customer
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Data Mining throughout the Customer
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Data Mining throughout the Customer
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Data Mining throughout the Customer
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Data Mining throughout the Customer
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Data Mining throughout the Customer
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CHAPTER 15 Data Warehousing, OLAP,
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Data Warehousing, OLAP, and Data Mi
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Data Warehousing, OLAP, and Data Mi
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Data Warehousing, OLAP, and Data Mi
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Data Warehousing, OLAP, and Data Mi
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Data Warehousing, OLAP, and Data Mi
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Data Warehousing, OLAP, and Data Mi
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Data Warehousing, OLAP, and Data Mi
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Data Warehousing, OLAP, and Data Mi
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Data Warehousing, OLAP, and Data Mi
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Data Warehousing, OLAP, and Data Mi
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Data Warehousing, OLAP, and Data Mi
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CHAPTER 16 Building the Data Mining
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Building the Data Mining Environmen
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Building the Data Mining Environmen
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Building the Data Mining Environmen
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Building the Data Mining Environmen
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Building the Data Mining Environmen
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Building the Data Mining Environmen
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Building the Data Mining Environmen
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Building the Data Mining Environmen
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Building the Data Mining Environmen
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Building the Data Mining Environmen
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Building the Data Mining Environmen
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Building the Data Mining Environmen
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CHAPTER 17 Preparing Data for Minin
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Preparing Data for Mining 541 a sig
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Preparing Data for Mining 543 Histo
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Preparing Data for Mining 545 data
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Preparing Data for Mining 547 varia
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Preparing Data for Mining 549 Figur
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Preparing Data for Mining 551 7,000
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Preparing Data for Mining 553 Chara
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Preparing Data for Mining 555 Ameri
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Preparing Data for Mining 557 Our r
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Preparing Data for Mining 559 Once
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Preparing Data for Mining 561 RESI
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Preparing Data for Mining 563 This
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Preparing Data for Mining 565 error
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Preparing Data for Mining 567 10,00
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Preparing Data for Mining 569 The f
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Preparing Data for Mining 571 Somet
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Preparing Data for Mining 573 PIVOT
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Preparing Data for Mining 575 ■
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Preparing Data for Mining 577 Purch
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Preparing Data for Mining 579 56 54
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Preparing Data for Mining 581 Data
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Preparing Data for Mining 583 Estim
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Preparing Data for Mining 585 There
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Preparing Data for Mining 587 This
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Preparing Data for Mining 589 Does
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Preparing Data for Mining 591 Becau
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Preparing Data for Mining 593 WARNI
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Preparing Data for Mining 595 ■
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CHAPTER 18 Putting Data Mining to W
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Putting Data Mining to Work 599 Wha
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Putting Data Mining to Work 601 A S
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Putting Data Mining to Work 603 a c
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Putting Data Mining to Work 605 Thi
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Putting Data Mining to Work 607 bas
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Putting Data Mining to Work 609 com
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Putting Data Mining to Work 611 At
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Putting Data Mining to Work 613 ■
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Index A absolute values, distance f
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Index 617 variance, 138 z-values, 1
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Index 619 C calculations, probabili
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Index 621 business goals, formulati
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Index 623 sorting, by scores, 8 tel
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Index 625 for catalog response mode
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Index 627 expected churn, 118 exper
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Index 629 MBR (memory-based reasoni
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Index 631 marginal customers, 553 m
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Index 633 nearest neighbor techniqu
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Index 635 performance, classificati
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Index 637 Q quadratic discriminates
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Index 639 sorting customers by, 8 z
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Index 641 Gaussian mixture model, 3
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Index 643 variables data selection,