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.
Rezensionen / Stimmen
From the reviews:
"This multi-authored book reports the findings of a European Commission sponsored project . . The aim of the project was to bring together the two disciplines, or practices, of data mining and decision support . . This book . can be recommended highly, to researchers or practitioners in the information sciences wanting an insight into just what data mining and decision support are really about, and where their potential lies." (David Bawden, JDOC - Journal of Documentation, Vol. 61 (3), 2005)
Reihe
Auflage
Softcover reprint of the original 1st ed. 2003
Sprache
Verlagsort
Zielgruppe
Für Beruf und Forschung
Research
Illustrationen
Maße
Höhe: 235 mm
Breite: 155 mm
Dicke: 17 mm
Gewicht
ISBN-13
978-1-4613-5004-0 (9781461350040)
DOI
10.1007/978-1-4615-0286-9
Schweitzer Klassifikation
1. Data Mining.- 2. Text and Web Mining.- 3. Decision Support.- 4. Integration of Data Mining and Decision Support.- 5 Collaboration in a Data Mining Virtual Organization.- 6. Data Mining Processes and Collaboration Principles.- 7. Decision Support for Data Mining: An Introduction to Roc Analysis and Its Applications.- 8. Data Mining for Decision Support: Supportingmarketing Decisions Through Subgroup Discovery.- 9. Preprocessing for Data Mining and Decision Support.- 10 Data Mining and Decision Support Integration Through the Predictive Model Markup Language Standard and Visualization.- 11 Analysis of Slovenian Media Space.- 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.- 14 Web Site Access Analysis for a National Statistical Agency.- 15 Five Decision Support Applications.- 16. Large and Tall Buildings: A Case Study in the Application of Decision Support and Data Mining.- 17 A Combined Data Mining and Decision Support Approach to Educational Planning.- 18 Collaborative Data Mining with Ramsys and Sumatra TT: prediction of Resources for a Health Farm.- 19 Collaborative Decision Making: An Environmental Case Study.- 20 Lessons Learned From Data Mining, Decision Support and Collaboration.- 21. Internet Support to Collaboration: A KnowledgeManagement and Organizational Memory View.- 22. Mind the Gap: Academia-Business Partnership Models and E-Collaboration Lessons Learned.
