Decision theoretic fusion framework for actionability using data mining on an embedded system

Heungkyu Lee, Sunmee Kang, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 multimodal interface. The proposed framework can make interactive data mining flexible, effective, and instantaneous in extracting the proper action item.

Original languageEnglish
Title of host publicationData Mining
Subtitle of host publicationTheory, Methodology, Techniques, and Applications
PublisherSpringer Verlag
Pages90-104
Number of pages15
ISBN (Print)3540325476, 9783540325475
DOIs
Publication statusPublished - 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3755 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Data mining
  • Embedded data mining
  • Speech interactive approach

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lee, H., Kang, S., & Ko, H. (2006). Decision theoretic fusion framework for actionability using data mining on an embedded system. In Data Mining: Theory, Methodology, Techniques, and Applications (pp. 90-104). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3755 LNAI). Springer Verlag. https://doi.org/10.1007/11677437_8