“新零售”概念的提出，使传统零售行业面临着很大的挑战，尤其是互联网的发展推动着零售业的转型。购物篮分析是指运用数据挖掘技术对顾客的历史购物记录进行分析，可以帮助超市有效的选择促销活动和制定合理的营销策略。本论文主要对某超市的购物篮数据进行探索性分析，为超市的营销活动提供决策支持。为了提高探索的准确性，借助RFM模型和决策树技术对顾客进行细分，帮助该超市找准客户细分的市场，并借助关联分析方法挖掘出不同商品之间的联系，更加有效的将商品推荐给需要的顾客，帮助商家留住现有顾客，吸引新顾客，从而提高销量并促进盈利。首先，本论文根据顾客的历史交易记录数据，使用RFM分析法对顾客进行细分，然后采用决策树技术建立预测模型，帮助超市识别顾客所属类型，最后使用关联分析挖掘出顾客购物篮中有意义的关联规则，进而帮助超市合理的安排货架上的商品摆放，并为超市提出选择性的营销方案，提高商品的销量。

The concept of "new retail" has made the traditional retail industry facing great challenges. In particular, the development of the Internet has driven the transformation of the retail industry. The analysis of the market basket uses data mining technology to analyze the customer history shopping records, which can help the supermarket to effectively select promotional activities and formulate appropriate marketing strategies. This thesis mainly analyzes the shopping basket data of a supermarket and provides decision support for the marketing activities of the supermarket. In order to improve the accuracy of exploration, the use of RFM model and decision tree technology to subdivide customers, help the supermarket to identify the market of customer segmentation, use association analysis methods to dig out the links between different goods, more effectively recommend products to Customers, help businesses retain existing customers, attract new customers, thereby increasing sales and promoting profitability. Firstly, based on customer's historical transaction record data, the paper uses RFM analysis method to subdivide customers, then uses decision tree technology to build forecasting models, helps supermarkets identify the type of customers, and finally uses association analysis to discover the existence of products purchased by customers. The meaningful association rules, in turn, help the supermarket to arrange the goods on the shelf reasonably, and propose selective marketing solutions for the supermarkets to promote the sales of the goods, improve the sales of goods.