Data mining method, device and system

Abstract

The invention provides a data mining method, device and system, and relates to the field of big data. The data mining method provided by the invention comprises the following steps: obtaining predetermined behavior data of users; classifying the users according to the generation time of the predetermined behavior data of each user and the number of the predetermined behavior data to determine a target user set; generating a single user feature vector of each user in the target user set according to the predetermined behavior data; and grading the target user set based on a clustering algorithm according to the single user feature vector to determine a grading user set. By adoption of such method, the users can be classified at first, user clustering is carried out in one category, so that appropriate target users can be selected to carry out clustering analysis, on one hand, better pertinence is guaranteed, and the operation data size is reduced, on the other hand, the interference of user data of different types to the clustering effect can be eliminated, and thus the user group division is more accurate.

Description

technical field [0001] The invention relates to the field of big data, in particular to a data mining method, device and system. Background technique [0002] In the field of big data applications, user groups can often be divided into several categories according to various behavioral characteristics of users, so as to provide precise and personalized services according to the characteristics of user groups. Clustering is a way to divide user groups. Clustering is the process of dividing data objects into classes, so that objects in the same class have a high degree of similarity, while objects in different classes are highly dissimilar. Dissimilarity is usually measured using distance. Cluster analysis has been widely used in various fields, such as market research, data analysis, pattern recognition and so on. [0003] However, the effect of dividing user groups based on user behavior characteristics in the clustering operation depends to a large extent on the quality ...

AI Technical Summary

Problems solved by technology

[0003] However, the effect of dividing user groups based on user behavior characteristics in the clustering operation depends to a large extent on the quality of basic data, and the existing user group division based on clustering algorithms often cannot reflect user behavior well. characteristics, there is the problem of inaccurate clustering, and it is difficult to use the clustering results to provide accurate and personalized services to user groups

Images