A New Algorithm for Discovery Maximal Frequent Itemsets
Source: Universiti Putra Malaysia
Since the introduction of the Apriori algorithms, frequent pattern mining plays an important role in data mining research. The problem of mining all frequent itemsets is that if there is a large frequent itemset with size L, then almost all 2L candidate subsets of the items might be generated. There are many contributions to enhance performance of mining all frequent itemsets. They have been mostly done base on three basic frequent itemsets mining methodologies: Apriori, FP-growth and Eclat. In real application, the number of frequent itemsets produced from a transaction database can be very huge and it becomes impossible to find all frequent itemsets.