Enterprise talent drainage data mining analysis method and device

Abstract

The invention discloses an enterprise talent drainage data mining analysis method and device. The device at least comprises a data source module, an employee classification module and an employee drainage rule analysis module, wherein employee information data collected by an enterprise remuneration system and a human resource system is preprocessed, cleared, integrated and converted by the data source module, and is stored into a data collecting server, the employee classification module uses a cluster classification analysis method for carry out cluster analysis on the data of the data source module, the employee clustering is carried out, and the employee clustering result is obtained; the employee drainage rule analysis module utilizes an employee-drainage-analysis-oriented association rule model for associating the employee classification information and the on-job and drainage state according to the clustering analysis result, and carries out causal relationship analysis on factors causing the employee drainage. The method and the device have the advantages that the mold enterprise talent drainage can be analyzed and predicted to obtain the talent drainage rule, meanwhile, the drainage rate of each employee can be obtained, and the reference remuneration of the employee is worked out for the employees with the high drainage rate.

Description

technical field [0001] The invention relates to the technical field of data analysis, in particular to a method and device for data mining and analysis of brain drain in mold companies. Background technique [0002] Mold is known as the "mother of industry", and it is one of the important forming methods in the manufacturing industry. It can be seen that the mold industry occupies an important position in modern industry. However, mold products have complex and changeable characteristics, making experienced mold technicians play an important role in mold manufacturing, so it is indispensable to analyze mold brain drain in mold companies. [0003] In an enterprise, new employees can make up for the loss of old employees, but it takes long-term training and accumulation of experience to truly replace the lost old employees and mold talents. This process will increase the cost of recruitment, the time cost of personnel training and the cost of training for the company. Compar...

AI Technical Summary

Problems solved by technology

[0010] (1) The analysis rules formulated by interviews and questionnaires are subject to the subjective influence of analysts and cannot objectively reflect the facts;

[0011] (2) Since the interview needs to mention the employee’s privacy-related thoughts on interpersonal relationships or the working environment, the employee will have reservations in the specific expression and there will be information errors;

[0012] (3) The reasons for resignation can be obtained through interviews and questionnaires, but they are only relatively superficial and extensive;

[0013] (4) The design and layout of the questionnaire content to the statistical analysis questionnaire requires a lot of manpower and material resources, which is contrary to the principle of high efficiency of the enterprise

[0016] 1) When there are many employees in an enterprise, it is difficult to classify and manage employees;

[0017] 2) It is difficult to describe the current situation of the company's existing employees from a comprehensive perspective;

[0018] 3) It is difficult to summarize the employee characteristics of the resigned employees;

[0019] 4) It is impossible to predict the possibility of employees leaving in the short term;

[0020] 5) Due to the large differences in various employee categories, it is difficult to formulate a reasonable reference salary

[0021] In view of the above five problems, it is difficult for traditional management science to draw scientific results

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