An Algorithm For Maximal Frequent Itemset Mining From Large Databases Using Bit-Set Representation Scheme Tiwari Akhilesh1,*, Gupta R.K2, Agrawal D.P.3 1Assistant Professor, Department of CSE & IT, Madhav Institute of Technology & Science, Gwalior-474005, (MP) 2Madhav Institute of Technology & Science, Gwalior-474005, MP, India 3Union Public Service Commission, New Delhi, India *amity.tiwari@rediffmail.com
Abstract The proposed algorithm, used for mining maximal frequent itemsets, finds interesting correlations between data in an efficient manner and can be helpful in case of large databases where large frequent itemsets contain large number of attributes. The algorithm used, works efficiently when the number of items in the large frequent itemsets is close to the number of total attributes in the dataset, or if the number of items in the large frequent itemsets is predetermined. The salient feature of the proposed approach is that it always automatically satisfies downward closure property, which states that all subsets of a frequent set must also be frequent. Owing to bit-set representation of dataset and utilization of unique list structure, the process of counting the occurrences of itemsets becomes faster, which ultimately enhances the overall mining performance. Top Keywords Maximal Frequent Itemset, Apriori Algorithm, Frequent Itemset Mining, Data Mining, Association Rule. Top |