A Survey on Mining High Utility Itemsets from Transactional Databases

Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Frequent itemset mining (FIM) is one of the most fundamental problems in data mining. In this work, we propose a novel strategy based on the analysis of item co-occurrences to reduce the number of join operations that need to be performed (FHM: Faster High-Utility Miner). A better approach in which we characterize a differentially private FIM algorithm based on the FP-growth algorithm, which is referred to as PFP-growth. The PFP-growth algorithm consists of a preprocessing phase and a mining phase. AS another commitment, we incorporate utility into sequential pattern mining, and a generic framework for high utility sequence mining is defined. An efficient algorithm, USpan, is presented to mine for high utility sequential patterns.