Assocaition Rule Mining with Itemwise Support Thresholds
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چکیده
Most of the association rule mining algorithm works based on the assumption that the items present in the dataset are of same kind with similar frequencies. Hence, the algorithms use levelwise support thresholds for mining. When the itemsets are of different frequency and of varied importance, the levelwise support thresholds are not suitable to discover frequent associations. Each item in a level reflects different characteristics and regularities. To produce low support and high confidence association rules, it is necessary to specify support thresholds for each item.
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