Abstract
Association rule mining is an important research topic in the data mining community. There are two difficulties occurring in mining association rules. First, the user must specify a minimum support for mining. Typically it may require tuning the value of the minimum support many times before a set of useful association rules could be obtained. However, it is not easy for the user to find an appropriate minimum support. Secondly, there are usually a lot of frequent itemsets generated in the mining result. It will result in the generation of a large number of association rules, giving rise to difficulties of applications. In this paper, we consider mining top- k frequent closed itemsets from data streams using a sliding window technique. A single pass algorithm, called FCI_ max, is developed for the generation of top- k frequent closed itemsets of length no more than max_ l. Our method can efficiently resolve the mentioned two difficulties in association rule mining, which promotes the usability of the mining result in practice.
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