Fuzzy data mining for interesting generalized association rules

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 thus introduce the problem of mining fuzzy generalized association rules from quantitative data. A fuzzy mining algorithm based on Srikant and Agrawal's method is proposed for extracting implicit generalized knowledge from transactions stored as quantitative values. It integrates fuzzy-set concepts and generalized data mining technologies to achieve this purpose. Items in rules may be from any level of the given taxonomy. The effect of numbers of fuzzy regions on the performance of the proposed algorithm is also discussed.