- Page 1: TEAMFLY
- Page 8: Vice President and Executive Group
- Page 14: Acknowledgments We are fortunate to
- Page 18: About the Authors Michael J. A. Ber
- Page 22: Introduction The first edition of D
- Page 26: Introduction xxv large samples and
- Page 32: vi Contents How Data Mining Is Bein
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- Page 40: x Contents How a Decision Tree Is G
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xvi Contents Chapter 16 Star Schema
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xviii Contents Chapter 18 Index Exa
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2 Chapter 1 Well-run small business
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4 Chapter 1 Bean or a satin bra fro
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6 Chapter 1 the right questions, an
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8 Chapter 1 Data mining is largely
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10 Chapter 1 imagine that the ski b
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12 Chapter 1 Profiling Sometimes th
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14 Chapter 1 Every Business Is a Se
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16 Chapter 1 to obtain the card, sh
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18 Chapter 1 customers should get t
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CHAPTER 2 The Virtuous Cycle of Dat
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The Virtuous Cycle of Data Mining 2
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The Virtuous Cycle of Data Mining 2
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The Virtuous Cycle of Data Mining 2
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The Virtuous Cycle of Data Mining 2
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The Virtuous Cycle of Data Mining 3
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The Virtuous Cycle of Data Mining 3
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The Virtuous Cycle of Data Mining 3
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The Virtuous Cycle of Data Mining 3
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The Virtuous Cycle of Data Mining 3
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The Virtuous Cycle of Data Mining 4
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CHAPTER 3 Data Mining Methodology a
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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Data Mining Methodology and Best Pr
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CHAPTER 4 Data Mining Applications
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Data Mining Applications 89 Data mi
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Data Mining Applications 91 are “
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Data Mining Applications 93 TIP Whe
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Data Mining Applications 95 DATA BY
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Data Mining Applications 97 mining
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Data Mining Applications 99 ROC CUR
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Data Mining Applications 101 BENEFI
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Data Mining Applications 103 How th
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Data Mining Applications 105 $400,0
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Data Mining Applications 107 before
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Data Mining Applications 109 Start
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Data Mining Applications 111 can be
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Data Mining Applications 113 The bi
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Data Mining Applications 115 Costs
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Data Mining Applications 117 and so
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Data Mining Applications 119 From a
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Data Mining Applications 121 to ass
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CHAPTER 5 The Lure of Statistics: D
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The Lure of Statistics: Data Mining
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Cumulative Proportion The Lure of S
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The Lure of Statistics: Data Mining
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The Lure of Statistics: Data Mining
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The Lure of Statistics: Data Mining
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The Lure of Statistics: Data Mining
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The Lure of Statistics: Data Mining
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The Lure of Statistics: Data Mining
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The Lure of Statistics: Data Mining
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The Lure of Statistics: Data Mining
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The Lure of Statistics: Data Mining
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The Lure of Statistics: Data Mining
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The Lure of Statistics: Data Mining
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166 Chapter 6 rule (such as income
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168 Chapter 6 1 1 lifetime orders <
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170 Chapter 6 For many applications
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172 Chapter 6 Finding the Splits At
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174 Chapter 6 Splitting on a Catego
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176 Chapter 6 claims were paid auto
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178 Chapter 6 Purity measures for e
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180 Chapter 6 To calculate the tota
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182 Chapter 6 COMPARING TWO SPLITS
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184 Chapter 6 does for continuous v
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186 Chapter 6 COMPARING MISCLASSIFI
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188 Chapter 6 COMPARING MISCLASSIFI
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190 Chapter 6 Error Rate Prune here
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192 Chapter 6 Miner using its defau
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194 Chapter 6 Watch the game? No Ye
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196 Chapter 6 Table 6.1 All Possibl
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198 Chapter 6 space, the correspond
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200 Chapter 6 Last Movie in Group L
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202 Chapter 6 TEAMFLY Figure 6.15 A
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204 Chapter 6 Figure 6.16 Miner. A
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206 Chapter 6 Simulating the Future
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208 Chapter 6 USING DECISION TREES
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CHAPTER 7 Artificial Neural Network
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Artificial Neural Networks 213 This
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Artificial Neural Networks 215 The
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Artificial Neural Networks 217 Tabl
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Artificial Neural Networks 219 Neur
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Artificial Neural Networks 221 inpu
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Artificial Neural Networks 223 outp
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Artificial Neural Networks 225 SIGM
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Artificial Neural Networks 227 The
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Artificial Neural Networks 229 to c
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Artificial Neural Networks 231 The
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Artificial Neural Networks 233 This
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Artificial Neural Networks 235 TIP
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Artificial Neural Networks 237 can
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Artificial Neural Networks 239 The
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Artificial Neural Networks 241 Othe
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Artificial Neural Networks 243 The
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Artificial Neural Networks 245 or d
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Artificial Neural Networks 247 Tabl
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Artificial Neural Networks 249 Self
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Artificial Neural Networks 251 The
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Artificial Neural Networks 253 ther
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Artificial Neural Networks 255 can
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258 Chapter 8 obvious geometric int
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260 Chapter 8 The first stage of MB
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262 Chapter 8 One possible combinat
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264 Chapter 8 1 0.9 0.8 0.7 0.6 0.5
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266 Chapter 8 What Are the Codes? T
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268 Chapter 8 USING RELEVANCE FEEDB
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270 Chapter 8 Choosing the Number o
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272 Chapter 8 3. Commutativity. Dir
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274 Chapter 8 MEASURING THE EFFECTI
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276 Chapter 8 Gender is an example
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278 Chapter 8 Furthermore, there is
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280 Chapter 8 In Table 8.12, the fi
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282 Chapter 8 Table 8.16 Confidence
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284 Chapter 8 Comparing Profiles On
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286 Chapter 8 produces better resul
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288 Chapter 9 In this shopping bask
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290 Chapter 9 The order is the fund
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292 Chapter 9 Order Characteristics
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294 Chapter 9 450 400 Mail Drop 350
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296 Chapter 9 Association Rules One
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298 Chapter 9 explanation: Is the d
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300 Chapter 9 This simple co-occurr
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302 Chapter 9 Detergent 1 0 0 1 1 S
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304 Chapter 9 Table 9.3 Transaction
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306 Chapter 9 The number of combina
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308 Chapter 9 Data Quality The data
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310 Chapter 9 Table 9.6 Confidence
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312 Chapter 9 For instance, in the
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314 Chapter 9 A pizza restaurant ha
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316 Chapter 9 TIP Adding virtual tr
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318 Chapter 9 Sequential Analysis U
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320 Chapter 9 Market basket analysi
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322 Chapter 10 often yields very in
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324 Chapter 10 Oops! These edges in
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326 Chapter 10 A C D Pregel River N
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328 Chapter 10 leaves the car in th
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330 Chapter 10 Directed Graphs The
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332 Chapter 10 The Kleinberg Algori
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334 Chapter 10 Identifying the Cand
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336 Chapter 10 Hubs and Authorities
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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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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