Application of Fractal Analysis for Customer Classification Based on Path Data
Application of Fractal Analysis for Customer Classification Based on Path Data
复制标题
分形分析在基于路径数据的客户分类中的应用
DOI：
10.1109/icdmw53433.2021.00040
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Yuta Kaneko
中科院分区：
文献类型：
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作者：
Fengmei Sun;Licheng Zhao;Yi Zuo;Yuta Kaneko
消费者行为分析对零售商品营销具有重要意义。本文旨在建立一个包含顾客购物路径复杂性的顾客分类模型。首先，我们选择一个目标区域来捕获客户的移动路径数据。路径数据包含一系列坐标为（x, y）的点。我们通过从（x, y）坐标变换像素，将这些点绘制成路径图。在这一阶段，点按照时间顺序用线连接起来。其次，采用盒计数法计算各路径图的分形维数；第三，我们考虑高斯函数和分布相似度来改进k-最近邻（KNN）算法。在数值实验中，我们使用改进的KNN算法学习了基于分形维数和停留时间的顾客分类模型。与支持向量机（SVM）和传统KNN分类模型相比，改进的KNN客户分类模型准确率为0.925，f1得分为0.926。
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 F1-score of 0.926.