Safp: A New Self-Adaptive Algorithm for Frequent Pattern Mining

This article builds a robust algorithm by methodically combining two different mining algorithms on FP-tree while adjusting the mining strategy dynamically and automatically during a complete process of frequent pattern mining. This article firstly proposes the naive depth first search algorithm (NDFS) that is based on FP-tree, and then briefly analyzes its performance on different datasets. After that, a new self-adaptive algorithm (SAFP) is proposed, which combines the NDFS with the FP-growth by a dynamic mining strategy on conditional FP-trees. Experiments demonstrate that the SAFP is more robust and efficient than both the NDFS and the FP-growth on various datasets