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Generating learning sequences for decision makers through data mining and competence set expansion.

Yi-Chung Hu1, Ruey-Shun Chen, Gwo-Hshiung Tzeng

  • 1Inst. of Inf. Manage., Nat. Chiao Tung Univ., Hsinchu, Taiwan.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 5, 2008
PubMed
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Data mining discovers useful patterns, forming a competence set for decision problems. This method creates learning sequences to expand this set efficiently, boosting decision-maker confidence and problem-solving abilities.

Area of Science:

  • Decision Science
  • Data Mining
  • Knowledge Management

Background:

  • Decision problems require specific knowledge and skills, termed competence sets.
  • Lack of a competence set can lead to decision-maker uncertainty.
  • Acquiring necessary patterns efficiently is crucial for competence set expansion.

Purpose of the Study:

  • To propose a two-phase method for generating learning sequences to expand competence sets.
  • To leverage data mining for identifying useful patterns within relational databases.
  • To minimize learning costs during competence set expansion.

Main Methods:

  • Phase 1: Utilizes a novel data mining technique to identify a competence set of useful patterns.
  • Phase 2: Employs the minimum spanning table method for efficient competence set expansion with minimal learning cost.

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Main Results:

  • The proposed method effectively generates learning sequences for competence set expansion.
  • Data mining successfully identifies relevant patterns for decision problems.
  • The minimum spanning table method ensures cost-effective expansion of the competence set.

Conclusions:

  • The integrated approach of data mining and competence set expansion aids decision-makers.
  • This method enhances the ability to solve decision problems and improve decision quality.
  • It provides a structured way to acquire necessary knowledge and skills for effective decision-making.