Application analysis of data mining in shopping APP

Abstract

Dingdong Buying Vegetable is a cohort of emerging entities that have swiftly gained prominence within the business domain in recent times. The data mining function of its data platform APP is undeniably linked to the underlying factor contributing to its commercial success. This article examines the fundamental principles, practical manifestations, and pertinent research instances of data mining. It specifically centers on the primary interface of the Dingdong Buying Vegetable APP, scrutinizing its design and distinctive attributes tailored to specific customers. Furthermore, it conducts an in-depth analysis of the correlation between the platforms commercial success and its data mining functionality. The primary aspect in which the data mining function of the Dingdong Buying Vegetable APP is expected to be manifested is through extensive data mining. The process of mining client data and conducting a full comparison and analysis of sales data for all products sold is a highly intricate and exhaustive endeavor. The use of these data mining functions serves multiple purposes. Firstly, it efficiently identifies customers genuine requirements on the client side, enabling the recommendation of suitable products and fostering customer reliance on the application. Additionally, it facilitates accurate decision-making in marketing products on the product side by comparing and analyzing diverse data pertaining to the sold products. This approach helps prevent the sale of unsought items and effectively minimizes company expenditure.