Survey of Particle Swarm Optimization Algorithm for Association Rule Mining

Abstract

Association rule mining is an important area in data mining, considering the large scale, high dimen-sionality, modal diversity and type complexity of current data, traditional association rule mining algorithms cannot meet the needs of big data. The particle swarm optimization algorithm, as an efficient intelligent algorithm, provides a new solution and has been widely used in association rule mining field in recent years. This paper introduces the basic principle of swarm optimization algorithm and the basic concept of association rules, and reviews the research progress of the swarm optimization algorithm itself. Then, this paper further summarizes the researches of the swarm optimization algorithm in association rule mining problem, including common data conversion methods, coding methods, and evaluation indexes. These improved algorithms from related researches are compared with other algorithms widely used in association rule mining, and their advantages, disadvantages, and application scenarios are discussed. After that, the existing improvement algorithms are systematically classified according to its methods, such as parameter, variation, and hybrid algorithm improvements, and the application areas of particle swarm optimization algorithms in association rule mining are also summarized, such as shopping baskets, finances, medical, industrial productions and risk assessments. At last, based on the introduction of the latest research pro-gress in this field, further research directions are discussed by analyzing the existing problems.

Keywords

• association rule mining
• particle swarm optimization algorithm
• intelligent algorithm