Department of Information Engineering College, Capital Normal University, Beijing, China

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Abstract—This work proposes an efficient mining algorithm to find maximal frequent item sets from relational database. It adapts to large datasets.Itemset is stored in list with special structure. The two main lists called itemset list and Frequent itemset list are created by scanning database once for dividing maximal itemsets into two categories depending on whether the itemsets to achieve minimum support number. Sub itemsets whose superset is in itemset list are generated by recursion to make sure that each sub itemsets appeared before its superset. As current sub itemsets being joined to frequent itemset list, its sub itemsets are pruned from the itemset list. At last, all sub itemsets whose nearest superset is in frequent itemset list are pruned from the frequent itemset list to hold all maximal frequent itemsets.We compare our algorithms and FP-Growth by two sets of time-consuming experiments to prove the superiority of our efficient algorithm both not only with increasing datasets but also with changing mini-support.

Index Terms—data mining; relational database; maximal frequent item sets

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Cite: Lijuan Zhou and Zhang Zhang, "Efficient Mining Algorithms of Finding Frequent Datasets," Journal of Software vol. 7, no. 4, pp. 727-732, 2012.