基于动态数据的加权频繁项集挖掘算法

为解决在挖掘频繁项集过程中,因忽略不同项目间的重要程度而导致的挖掘有效性低以及忽略数据的动态更新而造成的挖掘效率低的问题,通过引入新的加权规则,从权值与频数两方面去体现项目间的重要性差异,并通过引入树形结构与关系矩阵提高数据动态变化时频繁项集的挖掘效率。创新性地提出基于动态数据的加权频繁项集挖掘算法weighted dynamic date mining (WDDM)。实验结果表明,WDDM算法较以往算法挖掘效率与有效性显著提高,有利于发现更多有研究价值的信息。

Weighted Frequent Itemsets Mining algorithm Based on dynamic data

In order to solve the problem of low mining efficiency caused by ignoring the importance of different items and ignoring the dynamic updating of data in the process of mining frequent itemsets. By introducing new weighting rules, the importance differences between items are reflected in terms of weights and frequencies, and the mining efficiency of frequent itemsets is improved by introducing tree structure and relational matrix. This paper innovatively proposes a weighted frequent itemset mining algorithm WDDM based on dynamic data. The experimental results show that the efficiency and validity of the WDDM algorithm are significantly improved compared with the previous algorithms, which is conducive to discovering more valuable information.