A systematic method to design a fuzzy data mining model
Yo‐Ping Huang, Ya-Hui Ke, Chi-Peng Ouyang, Kunling Lin · 2003
Based on the available transaction records, we use AprioriTid model to derive the association rules from large database. We then exploit association rules to establish an initial fuzzy inference model. A novel tuning method is proposed to adjust the fuzzy model such that every association rule from data mining model can in turn help us recommend the most appropriate products to the prospective customers. By combining the Larsen's inference method and gradient descent method, we derive a systematic approach to refine the fuzzy model. Thus, a new adjusting method, i.e., Larsen-like, is proposed in this paper. How to derive the association rules from large database, how to apply the derived rules to establishing a fuzzy inference model, and how to optimize the fuzzy model are illustrated by simple examples.