Data Mining and Decision Support: Integration and Collaboration / Edition 1
by Dunja Mladenic
Data mining deals with finding patterns in data that are by user-definition, interesting and valid. It is an interdisciplinary area involving databases, machine learning, pattern recognition, statistics, visualization and others. Decision support focuses on developing systems to help decision-makers solve problems. Decision support provides a selection of data
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Data mining deals with finding patterns in data that are by user-definition, interesting and valid. It is an interdisciplinary area involving databases, machine learning, pattern recognition, statistics, visualization and others. Decision support focuses on developing systems to help decision-makers solve problems. Decision support provides a selection of data analysis, simulation, visualization and modeling techniques, and software tools such as decision support systems, group decision support and mediation systems, expert systems, databases and data warehouses. Independently, data mining and decision support are well-developed research areas, but until now there has been no systematic attempt to integrate them. Data Mining and Decision Support: Integration and Collaboration, written by leading researchers in the field, presents a conceptual framework, plus the methods and tools for integrating the two disciplines and for applying this technology to business problems in a collaborative setting.
Product Details
- ISBN-13:
- 9781402073885
- Publisher:
- Springer US
- Publication date:
- 09/30/2003
- Series:
- The Springer International Series in Engineering and Computer Science, #745
- Edition description:
- 2003
- Pages:
- 275
- Product dimensions:
- 0.75(w) x 6.14(h) x 9.21(d)
Table of Contents
| Preface | ||
| Acknowledgments | ||
| Foreword | ||
| Contributing Authors | ||
| Pt. I | Basic Technologies | 1 |
| 1 | Data Mining | 3 |
| 2 | Text and Web Mining | 15 |
| 3 | Decision Support | 23 |
| 4 | Integration of Data Mining and Decision Support | 37 |
| 5 | Collaboration in a Data Mining Virtual Organization | 49 |
| 6 | Data Mining Processes and Collaboration Principles | 63 |
| Pt. II | Integration Aspects of Data Mining and Decision Support | 79 |
| 7 | Decision Support for Data Mining: An introduction to ROC analysis and its applications | 81 |
| 8 | Data Mining for Decision Support: Supporting marketing decisions through subgroup discovery | 91 |
| 9 | Preprocessing for Data Mining and Decision Support | 107 |
| 10 | Data Mining and Decision Support Integration through the Predictive Model Markup Language Standard and Visualization | 119 |
| Pt. III | Applications of Data Mining and Decision Support | 131 |
| 11 | Analysis of Slovenian Media Space | 133 |
| 12 | On the Road to Knowledge: Mining 21 years of UK traffic accident reports | |
| 13 | Analysis of a Database of Research Projects Using Text Mining and Link Analysis | 157 |
| 14 | Web Site Access Analysis for a National Statistical Agency | 167 |
| 15 | Five Decision Support Applications | 177 |
| 16 | Large and Tall Buildings: A case study in the application of decision support and data mining | 191 |
| 17 | A Combined Data Mining and Decision Support Approach to Educational Planning | 203 |
| Pt. IV | Collaboration Aspects | 213 |
| 18 | Collaborative Data Mining with Ramsys and Sumatra TT: Prediction of resources for a health farm | 215 |
| 19 | Collaborative Decision Making: An environmental case study | 227 |
| 20 | Lessons Learned from Data Mining, Decision Support and Collaboration | 237 |
| 21 | Internet Support to Collaboration: A knowledge management and organizational memory view | 247 |
| 22 | Mind the Gap: Academia-business partnership models and e-collaboration lessons learned | 261 |
| Subject index | 271 |
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