The potential employability in different applications has garnered more significance for Periodic High-Utility Itemset Mining (PHUIM). It is to be noted that the conventional utility mining algorithms focus on an itemset’s utility value rather than that of its periodicity in the transaction. A MEAN periodicity measure is added to the minimum (MIN) and maximum (MAX) periodicity to incorporate the periodicity feature into PHUIM in this proposed work. The MEAN-periodicity measure brings a new dimension to the periodicity factor and is arrived at by dividing itemset’s period value by the total number of transactions in that dataset. Further, an algorithm to mine Index-Based Periodic High Utility Itemset Mining (IBPHUIM) from the database using an indexing approach is also proposed in this paper. The proposed IBPHUIM algorithm employs a projection-based technique and indexing procedure to increase memory and execution speed efficiency. The proposed model avoids redundant database scans by generating sub-databases using an indexing data structure. The proposed IBPHUIM model has experimented with test datasets, and the results drawn show that the proposed IBPHUIM model performs considerably better.

不同应用程序中的潜在就业能力对定期高效项目集挖掘 (PHUIM) 具有更重要的意义。需要注意的是，传统的效用挖掘算法关注的是项目集的效用价值，而不是它在交易中的周期性。在这项建议的工作中，将 MEAN 周期性度量添加到最小 (MIN) 和最大 (MAX) 周期性，以将周期性特征合并到 PHUIM 中。MEAN 周期度量为周期因子带来了一个新维度，它是通过将项目集的周期值除以该数据集中的事务总数得出的。此外，本文还提出了一种使用索引方法从数据库中挖掘基于索引的周期性高效用项集挖掘（IBPHUIM）的算法。所提出的 IBPHUIM 算法采用基于投影的技术和索引程序来提高内存和执行速度效率。所提出的模型通过使用索引数据结构生成子数据库来避免冗余数据库扫描。所提出的 IBPHUIM 模型已经对测试数据集进行了实验，得出的结果表明所提出的 IBPHUIM 模型的性能要好得多。