This paper proposes the problem of mining weighted generalized fuzzy association rules with fuzzy taxonomies (WGF-ARs). It is an extension of the generalized fuzzy association rules with fuzzy taxonomies problem. In order to reflect the importance of different items, the notion of generalized weights is introduced, and leaf-node items and ancestor items are assigned generalized weights in our WGF-ARs. The definitions of weighted support and weighted confidence of WGF-ARs is also proposed. Then a new mining algorithm for WGF-ARs is also proposed, and several optimizations have been applied to reduce the computational complexity of the algorithm.
Weitere Kapitel dieses Buchs durch Wischen aufrufen
- Mining Weighted Generalized Fuzzy Association Rules with Fuzzy Taxonomies
- Springer Berlin Heidelberg