Abstract: This paper presents a new algorithm for mining weighted frequent item sets without generating candidate. A weight set of attributes is normalized to avoid weighted approval rate greater than 1. The new algorithm is testified to satisfy weighted downward closure property. An effectively mining pruning strategy based on weighed Fp-tree is structured. Example analysis and experimental results show that the algorithm can reduce the weighted frequent item sets formation process of computation, and improve weighted frequent item sets generation efficiency.

Key words: data mining, association rule, weighted frequent pattern, weighted Fp tree, weighted downward closure property

摘要： 提出一种不产生候选项目集的加权频繁模式挖掘算法。对每个项目集权重进行归一化操作，避免加权支持率大于1，证明该算法满足加权向下封闭性。在此基础上，构建基于加权Fp树的剪枝策略。实例分析和实验结果表明，该算法能减少加权频繁项目集生成过程中的计算量，提高加权频繁项目集的生成效率。

关键词: 数据挖掘, 关联规则, 加权频繁模式, 加权Fp树, 加权向下封闭性

CLC Number: 

• TP311