Mining fuzzy generalized association rules from quantitative data under fuzzy taxonomic structures

Due to the increasing use of very large databases and data warehouses, mining useful information and helpful knowledge from transactions has become an important research area. Most conventional data-mining algorithms identify the relationships among transactions using binary values and find rules at a single concept level. Transactions with quantitative values and items with taxonomic relations are, however, commonly seen in real-world applications. Besides, the taxonomic structures may not be crisp. This paper thus proposes a fuzzy data-mining algorithm for extracting fuzzy generalized association rules under given fuzzy taxonomic structures. The proposed algorithm first generates expanded transactions according to given fuzzy taxonomic structures. It then transforms each quantitative value into a fuzzy set in linguistic terms. Each item uses only the linguistic term with the maximum cardinality in later mining processes, thus making the number of fuzzy regions to be processed the same as that of the original items. The mining process based on fuzzy counts is then performed to find fuzzy generalized association rules from these items. The algorithm can therefore focus on the most important linguistic terms and reduce its time complexity.