MMC: efficient and effective closed high-utility itemset mining

Analyzing many high-utility itemsets using HUIM methods is challenging and time-consuming. To solve this issue, Closed High-Utility Itemsets (CHUI), a condensed and lossless representation of high-utility itemsets, have been proposed.

Using a matrix to hold both the total value of the transactions and the value of each item in transactions, we describe a new approach for the CHUI problem in this work. By merging the items and removing unnecessary values, one can use this matrix to enhance the mining of closed high-utility itemsets. The pruning approach in the suggested algorithm compares the aggregate value of the items with the minimum value, which can result in a more effective use of time and space. In contrast to other algorithms, which could only establish a minimum value for all the items, this method can set a minimum value for each item, which is one of its surprises. In addition to only scanning the database once, this approach is more straightforward and up to three times more time- and space-efficient than the prior algorithms.