Research In Motion
Frequent itemsets generation is an important area of data mining. This paper is concerned with applying progressive approach to extract interesting information from a static database using dynamic approach. This provides an intelligent environment to discover frequent itemsets while reading a particular set of transaction from static database. The authors performed extensive experiments and calculate the execution time to generate frequent itemsets on the basis of support and number of transaction read at a time.