Efficient mining frequent itemsets algorithms

Efficient algorithms for mining frequent itemsets are crucial for mining association rules as well as for many other data mining tasks. It is well known that countTable is one of the most important facility to employ subsets property for compressing the transaction database to new lower representation of occurrences items. One of the biggest problem in this technique is the cost of candidate generation and test processing which are the two most important steps to find association rules. In this paper, we have developed this method to avoid the costly candidate-generation-and-test processing completely. Moreover, the proposed methods also compress crucial information about all itemsets, maximal length frequent itemsets, minimal length frequent itemsets, avoid expensive, and repeated database scans. The proposed named CountTableFI and BinaryCountTableF are presented, the algorithm has significant difference from the Apriori and all other algorithms extended from Apriori. The idea behind this algorithm is in the representation of the transactions, where, we represent all transactions in binary number and decimal number, so it is simple and fast to use subset and identical set properties. A comprehensive performance study shows that our techniques are efficient and scalable comparing with other methods.