Efficient Mining of Frequent Closed Itemsets without Closure Checking

Abstract

Most existing algorithms for mining frequent closed itemsets have to check whether a newly generated itemset is a frequent closed itemset by using the subset checking technique. To do this, a storing structure is required to keep all known frequent itemsets and candidates. It takes additional processing time and memory space for closure checking. To remedy this problem, an efficient approach called closed itemset mining with no closure checking algorithm is proposed. We use the information recorded in an FP-tree to identify the items that will not constitute closed itemsets. Using this information, we can generate frequent closed itemsets directly. It is no longer necessary to check whether an itemset is closed or not when it is generated. We have implemented our algorithm and made many performance experiments. The results show that our approach has better performance in the runtime and memory space utilization. Moreover, this approach is also suitable for parallel mining of frequent closed itemsets.