Mining of High-Utility Itemsets by ACO Algorithms

Document Type

Conference Proceeding


High-utility itemset mining (HUIM) is a major contemporary data mining issue. It is different from frequent itemset mining (FIM), which only considers the quantity factor. HUIM applies both the quantity and profit factors to be used to reveal the most profitable products. In this paper, a novel algorithm based on a evolutionary computation technique, ant colony optimization (ACO), is proposed to resolve this issue. Unlike genetic algorithms (GAs) and particle swarm optimizations (PSOs), ACOs produce a feasible solution in a constructive way. They can avoid generating unreasonable solutions as much as possible. Thus, a well-defined ACO approach can always obtain suitable solutions efficiently. An ant colony system (ACS), which is extended from ACO, is proposed to efficiently find HUIs (HUIM-ACS). In general, an EC algorithm cannot make sure the provided solution is the global optimal solution. But the designed HUIM-ACS algorithm maps the completed solution space into the routing graph. Therefore, it guarantees that it obtains all of the HUIs when there is no candidate edge from the starting point. In addition, HUIM-ACS does not estimate the same feasible solution again in its process in order to avoidwasting computational resource. Substantial experiments on real-life datasets show that the proposed algorithm outperforms the other heuristic algorithms for mining HUIs in terms of the number of discovered HUIs, and convergence.