A Customer Classification Method Based on Grid and Density Fusion of Dynamic and Static Data

Abstract

The invention discloses a dynamic and static data fusion customer classification algorithm based on grid and density, which establishes a dimensionality-reduced customer clustering model by fusing two classification algorithms, and determines customer categories; for each type of customer text analysis, calculates each The frequency of each customer category, the frequency and attention degree corresponding to each customer category characteristic attribute, determine the symmetric uncertainty value between the focus point and the customer category, realize the customer category classification and service focus extraction, and complete the structured data and The integration and mining of unstructured data and the determination of corresponding relationships can complete the accurate analysis and precise positioning of customer behavior, so as to achieve targeted business recommendations for customers.

Description

technical field [0001] The invention relates to the technical field of natural language processing applications, in particular to a grid and density-based dynamic and static data fusion customer classification method. Background technique [0002] With the development of the Internet, machine learning and big data technology, various opportunities and challenges have been brought to physical stores and e-commerce. The diversification of shopping methods has greatly reduced the cost of customers choosing among enterprises, and their mobility has continued to increase. For better development, how different business models determine their own advantages, and how to accurately position and serve different types of customers will help enterprises develop better. As the end of the consumption chain, consumers are the final users of products, the final recipients of brand marketing and the final experiencers of terminal construction. It can be said that whether consumers are satisf...

AI Technical Summary

Problems solved by technology

The current problem is that it only pays attention to the purchase information of consumers while ignoring the evaluation information of consumers, and only pays attention to the growth of sales while ignoring the satisfaction of consumers. From the perspective of consumers, it can form reasonable guidance for its operation, and it cannot effectively take measures to improve the quality of service to consumers.

DBSCAN is a density-based clustering algorithm, which plays an important role for data with noise points. It does not need to input the number of categories k, and can find clusters of any shape, and can find outliers at the same time, but only for Dense number aggregation is effective

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