Decision Theoretic Fusion Framework for Actionability Using Data Mining on an Embedded System
| Autor: | Heungkyu Lee, Hanseok Ko, Sunmee Kang |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Association rule learning
Computer science business.industry Decision theory Interface (computing) Navigation system computer.software_genre Machine learning Sensor fusion Information extraction Knowledge extraction Embedded system Table (database) The Internet Data mining Artificial intelligence business computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783540325475 Selected Papers from AusDM |
| DOI: | 10.1007/11677437_8 |
| Popis: | This paper proposes a decision theoretic fusion framework for actionability using data mining techniques in an embedded car navigation system. An embedded system having limited resources is not easy to manage the abundant information in the database. Thus, the proposed system stores and manages only multiple level-of-abstraction in the database to resolve the problem of resource limitations, and then represents the information received from the Web via the wireless network after connecting a communication channel with the data mining server. To do this, we propose a decision theoretic fusion framework that includes the multiple level-of-abstraction approach combining multiple-level association rules and the summary table, as well as an active interaction rule generation algorithm for actionability in an embedded car navigation system. In addition, it includes the sensory and data fusion level rule extraction algorithm to cope with simultaneous events occurring from multi-modal interface. The proposed framework can make interactive data mining flexible, effective, and instantaneous in extracting the proper action item. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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