An approach for Association Rule Mining: Hash-Based Reverse Apriori using the Masking technique

Abstract

Consumer buying patterns are a type of purchase made by consumers, whether by an individual or many individuals, to get the desired thing by making a purchase transaction. Apriori algorithm helps to find patterns among purchased items. But for larger transaction datasets, Apriori has high time complexity, as the database is scanned multiple times with the help of expensive resources. This, in turn, impacts the algorithm when the computer memory is inadequate, and there are a voluminous number of frequent transactions. This study aims to create an optimization of Apriori that is used in determining consumer purchasing patterns. The principal objective of this exploration is to make an advanced proposal framework utilizing hash-based Apriori that can assist with investigating the purchasing behavior of clients and, in view of that, suggest the most appropriate items to them as per their needs.