Mining a Complete Set of Fuzzy Multiple-Level Rules

Most conventional data-mining algorithms identify relationships among transactions using binary values and find rules at a single-concept level. Transactions with quantitative values and items with hierarchical relationships are, however, commonly seen in real-world applications. In the past, we proposed a fuzzy multiple-level mining algorithm for extracting knowledge implicit in transactions stored as quantitative values. In that algorithm, each attribute uses only the linguistic term with the maximum cardinality in the mining process. The number of linguistic items is thus the same as that of the original attributes in the transaction database. This constraint allows the processing time as efficient as mining of non-fuzzy association rules. However, the fuzzy association rules derived in that way are not complete, as some possible fuzzy association rules might be missing. This paper proposes a new fuzzy data-mining algorithm for extracting all possible fuzzy association rules from transactions stored as quantitative values. The proposed algorithm can derive a more complete set of rules but with more computation time than the previous method. Trade-off thus exists between the computation time and the completeness of rules. Choosing an appropriate mining method thus depends on the requirement of the application domains.