Dynamic Fuzzy Machine Learning

Dynamic fuzzy association rule generation algorithm (DFCL_Rule):

(1)The dynamic fuzzy formal background is preprocessed to generate the dynamic fuzzy processing background. The corresponding dynamic fuzzy concept lattice is generated according to the dynamic fuzzy processing background.

(2)The breadth-first traversal of the corresponding dynamic fuzzy concept lattice is carried out, and each dynamic fuzzy concept node is judged. If $(\stackrel{\leftharpoonup }{\alpha },\stackrel{\rightharpoonup }{\alpha })$ is greater than the prior parameter $(\stackrel{\leftharpoonup }{\theta },\stackrel{\rightharpoonup }{\theta })\text{and}(\stackrel{\leftharpoonup }{\delta },\stackrel{\rightharpoonup }{\delta })$ is less than the prior parameter $(\stackrel{\leftharpoonup }{\psi },\stackrel{\rightharpoonup }{\psi }),$, then the dynamic fuzzy concept nodes are dynamic fuzzy frequent nodes.

(3)Enumerating all dynamic fuzzy node pairs $<({\stackrel{\leftharpoonup }{c}}_{\text{i}},{\stackrel{\rightharpoonup }{c}}_i),({\stackrel{\leftharpoonup }{c}}_j,{\stackrel{\rightharpoonup }{c}}_j)>$ in ...