Chance discovery
著者
書誌事項
Chance discovery
（Advanced information processing / series editor, Lakhmi C. Jain）
Springer, 2003
- : alk. paper
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
Chance discovery means discovering chances - the breaking points in systems, the marketing windows in business, etc. It involves determining the significance of some piece of information about an event and then using this new knowledge in decision making. The techniques developed combine data mining methods for finding rare but important events with knowledge management, groupware, and social psychology. The reader will find many applications, such as finding information on the Internet, recognizing changes in customer behavior, detecting the first signs of an imminent earthquake, etc.
This first book dedicated to chance discovery covers the state of the art in the theory and methods and examines typical scenarios, and it thus appeals to researchers working on new techniques and algorithms and also to professionals dealing with real-world applications.
目次
1. Modeling the Process of Chance Discovery.- 2. Decisions by Chance and on Chance: Meanings of Chance in Recent News Stories.- 3. Prediction, Forecasting, and Chance Discovery.- 4. Self-organizing Complex Systems.- 5. Anatomy of Rare Events in a Complex Adaptive System.- 6. Human-to-Human Communication for Chance Discovery in Business.- 7. Topic Diffusion in a Community.- 8. Dimensional Representations of Knowledge in an Online Community.- 9. Discovery of Tacit Knowledge and Topical Ebbs and Flows Within the Utterances of an Online Community.- 10. Agent Communications for Chance Discovery.- 11. Logics of Argumentation for Chance Discovery.- 12. Awareness and Imagination of Hidden Factors and Rare Events.- 13. Effects of Scenic Information.- 14. The Storification of Chances.- 15. The Prepared Mind: the Role of Representational Change in Chance Discovery.- 16. Abduction and Analogy in Chance Discovery.- 17. Active Mining with Visual Human Interface.- 18. KeyGraph: Visualized Structure Among Event Clusters.- 19. Discovering Deep Building Blocks for Competent Genetic Algorithms Using Chance Discovery via KeyGraphs.- 20. Enhancing Daily Conversations.- 21. Chance Discoveries from the WWW.- 22. Detection of Earthquake Risks with KeyGraph.- 23. Application to Questionnaire Analysis.- 24. Chance Discovery for Consumers.- 25. Application to Understanding Consumers' Latent Desires.- Author Index.
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