Top-N frequent itemsets mining gets the interesting results by appointing the quantity of the most frequent itemsets. It is an important data mining application. This article proposed a Top-N frequent itemsets mining algorithm based on greedy method. The obtained Top-N itemsets are stored in a static doubly linked list. Join two frequent itemsets which have the highest support in the not joined Top-N itemsets. The frequent itemsets have the higher support that can be generated early. We can adjust the border support quickly. According to the analysis and experiments, this new algorithm demonstrates better performance than Apriori and NApriori on the time and space performance.
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