基于矩阵的频繁模式挖掘及更新算法

Frequent Patterns Mining and Incremental Updating Algorithm Based on Matrix

频繁模式挖掘在数据挖掘领域已经有广泛的应用。然而,对于增量更新频繁模式挖掘研究得不是很多。本文提出了一种新颖的增量更新频繁模式树结构(IUNP_Tree),构建它只需要对数据库扫描一次。此外,提出了基于条件矩阵(conditional matrix)的频繁模式挖掘算法(FPBM_Mine)和增量更新算法INUPA,可以有效地处理数据库的增量更新问题。实验表明,该算法是有效的,并且运行效率高于FP-growth算法。

Frequent patterns mining has been studied popularly in KDD research. However, little work has been clone on incremental updating frequent patterns mining. A novel incremental updating pattern tree （INUP_Tree） structure is present is paper, which is constructed by scanning. database only once. Besides, a new frequent pattern mining method （FPBM_Mine） based on conditional matrix and incremental updating algorithms INUPA are developed. The experiment result shows that the FPBM_Mine method is more efficient and faster than the FP-growth.