Fuzzy Association Rules to Summarise Multiple Taxonomies in Large Databases
Trevor Martin, Yun Cheng Shen · IGI Global eBooks · 2010
When working with large datasets, a natural approach is to group similar items into categories (or sets) and summarise the data in terms of such categories. Fuzzy set theory allows us to represent and reason about sets of objects without providing crisp definitions for each group, an approach that often reflects the human interpretation of categories. Given two or more hierarchical sets of categories, our aim is to determine the correspondence between categories (e.g., approximate equivalence). Association rules are a useful tool in knowledge discovery from databases but are normally defined in terms of crisp rather than fuzzy categories. In this chapter, the authors describe a new method for calculating a fuzzy confidence value for association rules between fuzzy categories, using a novel approach based on mass assignment theory.