Utilizing Index‑Based Periodic High Utility Mining to Study Frequent Itemsets

The potential employability in diferent applications has garnered more signifcance 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 projectionbased technique and indexing procedure to increase memory and execution speed efciency. 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.