Mining fuzzy temporal knowledge from quantitative transactions

In this paper, we propose a data mining algorithm for deriving fuzzy temporal association rules. It first transforms each quantitative value into a fuzzy set using the given membership functions. Meanwhile, item lifespans are collected and recorded in a temporal information table during the transformation process. The algorithm then calculates the scalar cardinality of each linguistic term of each item. The mining process based on fuzzy counts and item lifespans is then performed to find fuzzy temporal association rules. Experiments on a simulation dataset are also made to show the effectiveness and the efficiency of the proposed approach.