Mining algorithm for association rules of power demand attribute dataset based on improved bee colony algorithm

In order to improve the mining accuracy of the electricity demand attribute dataset and ensure its application in power system management and scheduling, an improved bee colony algorithm based association rule mining algorithm for electricity demand attribute dataset is proposed. Establish Strongly correlated material rules for attribute data sets, use K-means clustering algorithm to Discretization the data with continuous values in the data set, and use the construction principle of MapReduce model to mine the attribute data set of power demand in parallel; Utilize bee colony algorithm to mine frequent itemsets, innovatively apply gravity algorithm to improve artificial bee colony algorithm, and optimize the mined frequent itemsets. The test results show that the acceleration ratio and non emptiness rate of using the algorithm in this paper for data mining reach 0.95 and 97.5%, respectively, and the directional mining accuracy reaches 98.5%, which has good practical application effects.