Abstract
Mining association rules is an important task in data mining. It discovers the hidden, interesting relationships (associations) between items in the database based on the user-specified support and confidence thresholds. In order to find relevant associations one has to specify an appropriate support threshold. The support threshold plays an important role in deciding the number of appropriate rules found. The rare associations will not appear if a high threshold is set. Some uninteresting associations may appear if a low threshold is set. This paper proposes an approach to obtain the appropriate support thresholds at each level of the level-wise mining approach. It sets the support threshold by analyzing the frequency of items and their associations in the database at each level. Experimental results show that this approach produces the interesting rules without specifying the user specified support threshold.
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