FIAST: A Novel Algorithm for Mining Frequent Itemsets

Abstract

An efficient algorithm to mine frequent itemsets 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 itemsets 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.

Mining Association Rules in Large Database by Implementing Pipelining Technique in Partition Algorithm

“…We represent the tidlist for an itemset l by l tid list .Clearly, the cardinality of the tid list of an itemset divided by the total number of transactions in a partition gives the support for that itemset in that partition. [9,11] Initially, the tid lists for 1-itemsets are generated directly by reading the partition. The tid list for a candidate k-itemset is generated by joining the tid lists of the two (k-1)-itemsets that were used to generate the candidate k itemset.…”