Mining fuzzy temporal association rules by item lifespans

• We propose a fuzzy temporal association rule mining algorithm (FTARM).
• Information inside transactions can be found correctly by using lifespan of items.
• Three datasets are used to show the FTARM is effective.
• Experiments show that FTARM can derive more rules than FAR.
• The derived rules are better than FAR in terms of supports and confidences.

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. In real-world applications, transactions may contain quantitative values and each item may have a lifespan from a temporal database. In this paper, we thus 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 through a transformation process. The algorithm then calculates the scalar cardinality of each linguistic term of each item. A mining process based on fuzzy counts and item lifespans is then performed to find fuzzy temporal association rules. Experiments are finally performed on two simulation datasets and the foodmart dataset to show the effectiveness and the efficiency of the proposed approach.

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