Fuzzy Weighted Data Mining from Quantitative Transactions with Linguistic Minimum Supports and Confidences

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 and set the minimum supports and minimum confidences at numerical values. Linguistic minimum support and minimum confidence values are, however, more natural and understandable for human beings. Transactions with quantitative values are also commonly seen in real-world applications. This paper thus attempts to propose a new mining approach for extracting linguistic weighted association rules from quantitative transactions, when the parameters needed in the mining process are given in linguistic terms. Items are also evaluated by managers as linguistic terms to reflect their importance, which are then transformed as fuzzy sets of weights. Fuzzy operations are then used to find weighted fuzzy large itemsets and fuzzy association rules. An example is given to clearly illustrate the proposed approach.