【摘要】 不同数据集中数据的不同分布特征,对于频繁模式挖掘算法往往有着较大影响。将不同的现有算法结合起来,根据数据集的不同特性采用不同的挖掘策略,有可能构造出鲁棒性强的新算法。本文首先提出了一种基于FP-tree的简单深度优先搜索算法NDFS,并简单分析了其在不同数据集上的特性。在分析的基础上,本文进一步将NDFS和经典的FP-growth算法进行结合,提出了一种在挖掘过程中根据局部空间特征动态采用不同策略的自适应算法SAFP。实验证明,SAFP算法在不同数据集上均能达到或优于原有最优算法的性能,具有较好的鲁棒性。更多还原

【Abstract】 The distinct characters of different datasets greatly influence the efficiency of specific methods in frequent pattern mining. It is possible to build a robust algorithm by methodically combining different algorithms that should be properly applied according to the characters of data distribution of current dataset. This paper firstly proposes the Naive Depth First Search algorithm (NDFS) that is based on FP-tree, and then briefly analyzes its performance on different datasets. Finally, 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.更多还原