Knowledge Discovery in a Framework for Modelling with Words

  • Zengchang Qin
  • Jonathan Lawry

The learning of transparent models is an important and neglected area of data mining. The data mining community has tended to focus on algorithm accuracy with little emphasis on the knowledge representation framework. However, the transparency of a model will help practitioners greatly in understanding the trends and idea hidden behind the system. In this chapter, a random set based knowledge representation framework for learning linguistic models is introduced. This framework is referred to as label semantics and a number of data mining algorithms are proposed. In this framework, a vague concept is modelled by a probability distribution over a set of appropriate fuzzy labels which is called as mass assignment. The idea of mass assignment provides a probabilistic approach for modelling uncertainty based on pre-defined fuzzy labels.


Bayesian Estimation Label Expression Focal Element Linguistic Rule Label Semantic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Zengchang Qin
    • 1
  • Jonathan Lawry
    • 2
  1. 1.Computer Science DivisionUniversity of CaliforniaBerkeleyUSA
  2. 2.Department of engineering MathematicsUniversity of BristolUK

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