Research And Application Of Association Rules Mining Based On DBS-PSO Optimization Algorithm

Association rule mining is one of the research priorities and hotspots in data mining technology.It has a wide range of applications in various fields.Apriori algorithm is one of the representative algorithms of association rules.Its performance is directly related to the efficiency of association analysis and conclusion.At present,in the face of explosive growth of various types of data,the problem of Apriori algorithm is increasingly prominent when it in dealing with them,it is mainly embodied in the some aspects:such as long running time,low efficiency,and the demand of setting the minimum support degree and the minimum confidence threshold by the subjective single.In recent years,scholars at home and abroad have studied the improvement and optimization of Apriori algorithm.Among them,the combination of Apriori algorithm and other intelligent algorithms is a research hotspot,and has made great achievements in this research direction.In this paper,we propose a DBS-PSO optimization algorithm to optimize the research,the basic idea is as follows:firstly,sampling the original data by the improved density deviation sampling algorithm to obtain the sample data;secondly,using the improved particle swarm optimization algorithm to obtain the solution space by setting the fitness function;finally,setting the solution space of the particle swarm algorithm as the threshold of minimum support and confidence in Apriori algorithm,then using the Apriori algorithm to acheive association rules mining during the sample data.The experimental results show that the DBS-PSO optimization algorithm proposed in this paper not only reduces the time running cost of Apriori algorithm,but also makes the mining of association rules more reasonable,objective and efficient.