Customer Behavior Analysis using Wild Horse Optimization Algorithm

One of the areas in which businesses use artificial intelligence techniques is the analysis and prediction of customer behavior. It is important for a business to predict the future behavior of its customers. In this paper, a customer behavior model using wild horse optimization algorithm is proposed. In the first step, K-Means algorithm is used to classify based on the features extracted from the time series, and then in the second step, wild horse optimization algorithm is used to estimate customer behavior. Three dataset including, the grocery store dataset, the household appliances dataset, and the supermarket dataset are used in the simulation. The best clusters count for the grocery store dataset, the household appliances dataset, and the supermarket dataset are obtained ۵, ۴, and ۴, respectively. The simulation results indicate that this proposed method is obtained the lowest prediction error in three simulated datasets and is superior to other counterparts.

Keywords:

Customers’ behavior analysis , Clustering , Time series features , Wild horse optimization