Mining from quantitative data with linguistic minimum supports and confidences
Tzung‐Pei Hong, Ming-Jer Chiang, Shyue-Liang Wang · 2003
Most conventional data-mining algorithms identify the relationships among transactions using binary values and set the minimum supports and minimum confidences at numerical values. 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 item sets and fuzzy association rules. An example is given to clearly illustrate the proposed approach.