Промышленный лизинг Промышленный лизинг  Методички 

1 [ 2 ] 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222


Team-Fly®



Introduction

The first edition of Data Mining Techniques for Marketing, Sales, and Customer Support appeared on book shelves in 1997. The book actually got its start in 1996 as Gordon and I were developing a 1-day data mining seminar for NationsBank (now Bank of America). Sue Osterfelt, a vice president at NationsBank and the author of a book on database applications with Bill Inmon, convinced us that our seminar material ought to be developed into a book. She introduced us to Bob Elliott, her editor at John Wiley & Sons, and before we had time to think better of it, we signed a contract.

Neither of us had written a book before, and drafts of early chapters clearly showed this. Thanks to Bobs help, though, we made a lot of progress, and the final product was a book we are still proud of. It is no exaggeration to say that the experience changed our lives - first by taking over every waking hour and some when we should have been sleeping; then, more positively, by providing the basis for the consulting company we founded, Data Miners, Inc. The first book, which has become a standard text in data mining, was followed by others, Mastering Data Mining and Mining the Web.

So, why a revised edition? The world of data mining has changed a lot since we starting writing in 1996. For instance, back then, Amazon.com was still new; U.S. mobile phone calls cost on average 56 cents per minute, and fewer than 25 percent of Americans even owned a mobile phone; and the KDD data mining conference was in its second year. Our understanding has changed even more. For the most part, the underlying algorithms remain the same, although the software in which the algorithms are imbedded, the data to which they are applied, and the business problems they are used to solve have all grown and evolved.



xxiv Introduction

Even if the technological and business worlds had stood still, we would have wanted to update Data Mining Techniques because we have learned so much in the intervening years. One of the joys of consulting is the constant exposure to new ideas, new problems, and new solutions. We may not be any smarter than when we wrote the first edition, but we do have more experience and that added experience has changed the way we approach the material. A glance at the Table of Contents may suggest that we have reduced the amount of business-related material and increased the amount of technical material. Instead, we have folded some of the business material into the technical chapters so that the data mining techniques are introduced in their business context. We hope this makes it easier for readers to see how to apply the techniques to their own business problems.

It has also come to our attention that a number of business school courses have used this book as a text. Although we did not write the book as a text, in the second edition we have tried to facilitate its use as one by using more examples based on publicly available data, such as the U.S. census, and by making some recommended reading and suggested exercises available at the companion Web site, www.data-miners.com/companion.

The book is still divided into three parts. The first part talks about the business context of data mining, starting with a chapter that introduces data mining and explains what it is used for and why. The second chapter introduces the virtuous cycle of data mining - the ongoing process by which data mining is used to turn data into information that leads to actions, which in turn create more data and more opportunities for learning. Chapter 3 is a much-expanded discussion of data mining methodology and best practices. This chapter benefits more than any other from our experience since writing the first book. The methodology introduced here is designed to build on the successful engagements we have been involved in. Chapter 4, which has no counterpart in the first edition, is about applications of data mining in marketing and customer relationship management, the fields where most of our own work has been done.

The second part consists of the technical chapters about the data mining techniques themselves. All of the techniques described in the first edition are still here although they are presented in a different order. The descriptions have been rewritten to make them clearer and more accurate while still retaining nontechnical language wherever possible.

In addition to the seven techniques covered in the first edition - decision trees, neural networks, memory-based reasoning, association rules, cluster detection, link analysis, and genetic algorithms - there is now a chapter on data mining using basic statistical techniques and another new chapter on survival analysis. Survival analysis is a technique that has been adapted from the small samples and continuous time measurements of the medical world to the



1 [ 2 ] 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222