Data mining is the process of extracting desirable knowledge or interesting
patterns from existing databases for specific purposes. Most conventional
data-mining algorithms identify the relationships among transactions using
binary values. Transactions with quantitative values are however commonly s
een in real-world applications. We proposed a fuzzy mining algorithm by whi
ch each attribute used only the linguistic term with the maximum cardinalit
y in the mining process. The number of items was thus the same as that of t
he original attributes, making the processing time reduced. The fuzzy assoc
iation rules derived in this way are not complete. This paper thus modifies
it and proposes a new fuzzy data-mining algorithm for extracting interesti
ng knowledge from transactions stored as quantitative values. The proposed
algorithm can derive a more complete set of rules but with more computation
time than the method proposed. Trade-off thus exists between the computati
on time and the completeness of rules. Choosing an appropriate learning met
hod thus depends on the requirement of the application domains.