FIAST: A Novel Algorithm for Mining Frequent Itemsets
Fudailah Duemong, Ladda Preechaveerakul, Sirirut Vanichayobon · 2009
An efficient algorithm to mine frequent item sets is crucial for mining association rules. Most of the previously used algorithms have generally been developed for using the computational time effectively, reducing the number of candidate itemsets and decreasing the number of scan in the database. However, the time can be reduced by aggregate transactions having similar itemsets. This paper, then proposes an efficient algorithm for mining frequent item sets without generating candidate itemsets called FIAST (frequent itemsets algorithm for similar transactions). The algorithm uses a divide-and-conquer method to reduce the task into the bitwise AND operation for finding itemsets and uses a depth-first search strategy to generate all frequent itemsets. The time complexity is analyzed by mathematical proof.