ARMFEG: Association rule mining by frequency-edge-graph for rare items

Abstract

Almost every global economy has now entered the digital era. Most stores are trading online and they are highly competitive. Traditional association rule mining is not suitable for online trading because information is dynamic and it needs a fast processing time. Sometimes products on online stores are out of stock or unavailable which needs to be manually addressed. Therefore, this work proposes a new algorithm called Association Rule Mining by Frequency-Edge-Graph (ARMFEG) that can convert transaction data to form a complete virtual graph and store items counting in the adjacency matrix. With the limitation of search space using the top weight which is automatically generated during frequency items generation, ARMFEG is very fast during the rule generation phase and can find association rules in all items from adjacency matrix which solves the rare item problem.