A Search Space Reduced Algorithm for Mining Frequent Patterns

Mining frequent patterns is to discover the groups of items appearing always together excess of a user specified threshold. Many approaches have been proposed for mining frequent patterns by applying the FP-tree structure to improve the efficiency of the FP-Growth algorithm which needs to recursively construct sub-trees. Although these approaches do not need to recursively construct many sub-trees, they also suffer the problem of a large search space, such that the performances for the previous approaches degrade when the database is massive or the threshold for mining frequent patterns is low. In order to reduce the search space and speed up the mining process, we propose an efficient algorithm for mining frequent patterns based on frequent pattern tree. Our algorithm generates a subtree for each frequent item and then generates candidates in batch from this sub-tree. For each candidate generation, our algorithm only generates a small set of candidates, which can significantly reduce the search space. The experimental results also show that our algorithm outperforms the previous approaches.