A novel method for discovering fuzzy sequential patterns using the simple fuzzy partition method
Journal of the American Society for Information Science and Technology
A fuzzy data mining algorithm for finding sequential patterns
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Finding useful fuzzy concepts for pattern classification using genetic algorithm
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
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For each decision problem, there is a competence set, proposed by Yu (1990), consisting of ideas, knowledge, information, and skills required for solving the problem. Thus, it is reasonable that we view a set of useful patterns discovered from a relational database by data mining techniques as a needed competence set for solving one problem. Significantly, when decision makers have not acquired the competence set, they may lack confidence in making decisions. In order to effectively acquire a needed competence set to cope with the corresponding problem, it is necessary to find appropriate learning sequences for acquiring those useful patterns, the so-called competence set expansion. This paper thus proposes an effective method consisting of two phases to generate learning sequences. The first phase finds a competence set consisting of useful patterns by using a proposed data mining technique. The other phase expands that competence set with minimum learning cost by the minimum spanning table method (Feng and Yu (1998)). From a numerical example, we can see that it is possible to help decision makers to solve the decision problems by use of the data mining technique and the competence set expansion, enabling them to make better decisions.