In this paper, we propose a novel mining task: mining frequent superset from the database of itemsets that is useful in bioinformatics, e-learning systems, jobshop scheduling, and so on. A frequent superset means that it contains more transactions than minimum support threshold. Intuitively, according to the Apriori algorithm, the level-wise discovering starts from 1-itemset, 2-itemset, and so forth. However, such steps cannot utilize the property of Apriori to reduce search space, because if an itemset is not frequent, its superset maybe frequent. In order to solve this problem, we propose three methods. The first is the Apriori-based approach, called Apriori-C. The second is Eclat-based approach, called Eclat-C, which is depth-first approach. The last is the proposed data complement technique (DCT) that we utilize original frequent itemset mining approach to mine frequent superset. The experiment study compares the performance of the proposed three methods by considering the effect of the number of transactions, the average length of transactions, the number of different items, and minimum support.
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