The fuzzy data mining generalized association rules for quantitative values

Due to the increasing use of very large databases and data warehouses, mining useful information and helpful knowledge from transactions is evolving into 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 hierarchy relation are, however, commonly seen in real-world applications. In this paper, we introduce the problem of mining generalized association rules for quantitative values. We propose fuzzy generalized rules mining algorithm for extracting implicit knowledge from transactions stored as quantitative values. Given a set of transaction and predefined taxonomy, we want to find fuzzy generalized association rules where the quantitative of items may be from any level of the taxonomy. 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 algorithm can therefore focus on the most important linguistic terms and reduce its time complexity. We propose algorithm combines fuzzy transaction data mining algorithm and mining generalized association rules algorithm. This paper related to set concepts, fuzzy data mining algorithms and taxonomy and generalized association rules.