Mining association rules with single and multi-objective grammar guided ant programming
Integrated Computer-Aided Engineering
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Association rules in data mining are used in order to discover latent information hidden in databases. The class of apriori algorithms provide a simple way to compute association rules. Despite the simplicity of the algorithm its computational complexity is quite high. Specifically, the computational performance degrades due to intense computational requirements during the phase of frequent itemset generation. In this paper we have proposed a novel procedure to extract frequent itemsets. The itemset is modeled as a cell of a hypercube, and this cell is represented as a lexicographical order combination. The procedure described in this paper reduces the number of database passes to a single pass to extract frequent itemsets. Thus, the procedure is computationally attractive.