| 000 | 00746camuuu200241 a 4500 | |
| 001 | 000001038531 | |
| 005 | 19991007094614.0 | |
| 008 | 970716s1998 as a b 001 0 eng | |
| 010 | ▼a 97030682 | |
| 020 | ▼a 1558604030 | |
| 040 | ▼a DLC ▼c DLC ▼d 244002 | |
| 049 | ▼l 151067877 | |
| 050 | 0 0 | ▼a QA76.9.D3 ▼b W445 1998 |
| 082 | 0 4 | ▼a 006.3 ▼2 20 |
| 090 | ▼a 006.3 ▼b W431p | |
| 100 | 1 | ▼a Weiss, Sholom M. |
| 245 | 1 0 | ▼a Predictive data mining : ▼b a practical guide / ▼c Sholom M. Weiss, Nitin Indurkhya. |
| 260 | ▼a San Francisco : ▼b Morgan Kaufmann Publishers, ▼c c1998. | |
| 300 | ▼a xii, 228 p. : ▼b ill. ; ▼c 23 cm. | |
| 504 | ▼a Includes bibliographical references and indexes. | |
| 650 | 0 | ▼a Database management. |
| 650 | 0 | ▼a Data mining. |
| 700 | 1 | ▼a Indurkhya, Nitin. |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Sejong Academic Information Center/Science & Technology/ | Call Number 006.3 W431p | Accession No. 151067877 (3회 대출) | Availability Loan can not(reference room) | Due Date | Make a Reservation | Service |
Contents information
Book Introduction
The potential business advantages of data mining are well documented in publications for executives and managers. However, developers implementing major data-mining systems need concrete information about the underlying technical principles?and their practical manifestations?in order to either integrate commercially available tools or write data-mining programs from scratch. This book is the first technical guide to provide a complete, generalized roadmap for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses.
Note: If you already own Predictive Data Mining: A Practical Guide, please see ISBN 1-55860-477-4 to order the accompanying software. To order the book/software package, please see ISBN 1-55860-478-2.
Reviews
"I enjoy reading PREDICTIVE DATA MINING. It presents an excellent perspective on the theory and practice of data mining. It can help educate statisticians to build alliances between statisticians anddata miners."
--Emanuel Parzen, Distinguished Professor of Statistics, Texas A&M University
Feature
+ Focuses on the preparation and organization of data and the development of an overall strategy for data mining.+ Reviews sophisticated prediction methods that search for patterns in big data.
+ Describes how to accurately estimate future performance of proposed solutions.
+ Illustrates the data-mining process and its potential pitfalls through real-life case studies.
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Table of Contents
CONTENTS Preface = xi 1 What Is Data Mining? = 1 1.1 Big Data = 2 1.1.1 The Data Warehouse = 3 1.1.2 Timelines = 6 1.2 Types of Data-Mining Problems = 7 1.3 The Pedigree of Data Mining = 11 1.3.1 Databases = 11 1.3.2 Statistics = 12 1.3.3 Machine Learnig = 13 1.4 Is Big Better? = 14 1.4.1 Strong Statistical Evaluation = 14 1.4.2 More Intensive Search = 14 1.4.3 More Controlled Experiments = 15 1.4.4 Is Big Necessary? = 15 1.5 The Tasks of Predicitive Data Mining = 16 1.5.1 Data Preparation = 16 1.5.2 Data Reduction = 18 1.5.3 Data Modeling and Prediction = 19 1.5.4 Case and Solution Analyses = 19 1.6 Data Mining : Art or Science? = 21 1.7 An Overview of the Bok = 21 1.8 Bibliographic and Historical Remarks = 22 2 Statistical Evalustion for Big Data = 25 2.1 The Idealized Model = 26 2.1.1 Classical Statistical Comparison and Evaluation = 27 2.2 It's Big but Is It Biased? = 30 2.2.1 Objective Versus Survey Data = 30 2.2.2 Significance and Purvey Data = 30 2.2.2.1 Too Many Comparisons? = 32 2.3 Classical Types of Statistical Prediction = 33 2.3.1 Predicting True-or-False : Classification = 34 2.3.1.1 Error Rates = 34 2.3.2 Forecasting Numbers : Regression = 34 2.3.2.1 Distance Measures = 35 2.4 Measuring Predictive Performance = 36 2.4.1 Independent Testing = 36 2.4.1.1 Random Training and Testing = 36 2.4.1.2 How Accurate Is the Error Estimate? = 38 2.4.1.3 Comparing Results for Error Measures = 39 2.4.1.4 Ideal or Real-World Sampling? = 41 2.4.1.5 Training and Testing from Different Time Periods = 43 2.5 Too Much Searching and Testing? = 45 2.6 Why Are Errors Made? = 47 2.7 Bibliographic and Historical Remarks = 49 3. Prearing the Data = 51 3.1 A Standard From = 52 3.1.1 Standard Measurements = 53 3.1.2 Goals = 55 3.2 Data Transformations = 55 3.2.1 Normalizations = 57 3.2.2 Data Smoothing = 58 3.2.3 Differences and Ratios = 60 3.3 Missing Data = 61 3.4 Time-Dependent Data = 62 3.4.1 Composing Features from Time Series = 67 3.4.2.1 Current Values = 68 3.4.2.2 Moving Averages = 68 3.4.2.3 Trends = 69 3.4.2.4 Seasonal Adjustments = 70 3.5 Hybrid Time-Dependent Applications = 71 3.5.1 Multivariate Time Series = 72 3.5.2 Classification and Time Series = 73 3.5.3 Standard Cases with Time-Series Attributes = 73 3.6 Text Mining = 74 3.7 Bibliogrphic and Historical Remarks = 78 4 Data Reduction = 81 4.1 Selecting the Best Features = 84 4.2 Feature Selection From Means and Variances = 86 4.2.1 Independent Features = 87 4.2.2 Distance-Based Optimal Feature Selection = 88 4.2.3 Heuristic Feature Selection = 90 4.3 Principal Components = 92 4.4 Feature Selection by Decision Trees = 95 4.5 How Many Measured Values? = 96 4.5.1 Reducing and Smothing Values = 98 4.5.1.1 Rounding = 101 4.5.1.2 K-Means Clustering = 102 4.5.1.3 Class Entropy = 104 4.6 How Many Cases? = 106 4.6.1 A Single Sample = 109 4.6.2 Incremental Samples = 111 4.6.3 Average Samples = 113 4.6.4 Specialized Case-Reduction Techniques = 115 4.6.4.1 Sequential Sampling over Time = 115 4.6.4.2 Strategic Sampling of Key Events = 116 4.6.4.3 Adjusting Prevalence = 116 4.7 Bibliographic and Historical Remarks = 117 5 Looking for Solutions = 119 5.1 Overview = 119 5.2 Math Solutions = 120 5.2.1 Linear Scoring = 120 5.2.2 Nonlinear Scoring : Neural Nets = 123 5.2.3 Advanced Statistical Methods = 128 5.3 Distance Solutions = 132 5.4 Logic Solutions = 135 5.4.1 Decision Trees = 136 5.4.2 Decision Rules = 138 5.5 What Do the Answers Mean? = 142 5.5.1 Is It Safe to Edit Solutions? = 144 5.6 Which Solution Is Preferable? = 145 5.7 Combining Differenct Answers = 146 5.7.1 Multiple Prediction Methods = 147 5.7.2 Multiple Samples = 148 5.8 Bibliographic and Historical Reamrks = 150 6 What's Best for Data Reduction and Mining? = 153 6.1 Let's Analyze Some Real Data = 154 6.2 The Experimental Methods = 158 6.3 The Empirical Results = 161 6.3.1 Significance Testing = 162 6.4 So What Did We Learn? = 162 6.4.1 Feature Selection = 163 6.4.2 Value Reduction = 167 6.4.3 Subsampling or All Cases? = 170 6.5 Graphical Trend Analysis = 172 6.5.1 Incremental Case Analysis = 173 6.5.2 Incremental Complexity Analysis = 176 6.6 Maximum Data Reduction = 181 6.7 Are There Winners and Losers in Performance? = 182 6.8 Getting the Best Results = 184 6.9 Bibliographic and Historical Remarks = 187 7 Art ro Science? Case Studies in Data Mining = 189 7.1 Why These Case Studies? = 190 7.2 A Summary of Tasks for Predictive Data Mining = 191 7.2.1 A Checklist Data Preparation = 192 7.2.2 A Checklist Data Reduction = 192 7.2.3 A Checklist Data Modeling and Prediction = 192 7.2.4 A Checklist Case and Solution Analyses = 193 7.3 The Case Studies = 193 7.3.1 Transaction Processing = 193 7.3.2 Text Mining = 197 7.3.3 Outcomes Analysis = 199 7.3.4 Process Control = 202 7.3.5 Marketing and User Profiling = 205 7.3.6 Exploratory Analysis = 207 7.4 Looking Ahead = 210 7.5 Bibliographic and Historical Remarks = 211 Appendix : Data-Miner Software Kit = 213 References = 215 Author Index = 223 Subject Index = 225