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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...
In this paper, we handle the problem of mining fuzzy temporal association rules from a publication database, and propose an algorithm to achieve it. In the algorithm, the lifespan of an item is measured by its entire publication periods in a publication database. Also, an itemset table structure is designed to effectively keep and efficiently obtain information of itemsets for mining. Finally, experiments...
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...
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