Trade-off between computation time and number of rules for fuzzy mining from quantitative data

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.