Application of Fractal Analysis for Customer Classification Based on Path Data.

Consumer behavior analysis is of great significance to retail merchandise marketing. This article aims to establish a customer classification model that includes the complexity of customer shopping paths. First, we select a target area to capture the customer's movement path data. The path data contains a series of points with (x, y) coordinates. We plot the points into a path map via transforming the pixels from the (x, y) coordinates. In this stage, the points are connected by lines according to the time sequence. Secondly, the box-counting method is used to calculate the fractal dimension of each path map. Thirdly, we considered Gaussian function and distribution similarity to improve k-nearest neighbor (KNN) algorithm. In numerical experiments, we use our improved KNN algorithm to learn a customer classification model based on fractal dimension and stay time. Compared with support vector machine (SVM) and traditional KNN classification models, our improved KNN customer classification model has higher accuracy of 0.925 and higher Fl-score of 0.926.