A Theoretical Formulation of Bit Mask Search Mining Technique for Mining Frequent Itemsets
In this paper, the authors have dealt with the problem of finding frequent itemsets which can be used to make strong association rules. Although a number of methods have been developed, those can mine frequent item sets. Association rule mining is a key issue in data mining. However, the classical models ignore the difference between the transactions, and the weighted association rule mining does not work on databases with only binary attributes. Emerging applications introduce the requirement for novel association-rule mining algorithms that will be scalable not only with respect to the number of records (number of rows) but also with respect to the domain's size (number of columns). In this paper, searching algorithms are closely related to the concept of dictionaries.