Application of Data Mining Technology in Human Resource Market

2022 ◽  
pp. 1731-1735
Author(s):  
Nanjue He
Keyword(s):  
Data Mining ◽  
Human Resource ◽  
Mining Technology

scholarly journals Influence of data mining technology in information analysis of human resource management on macroscopic economic management

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251483
Author(s):  
Ai Zhang
Keyword(s):  
Data Mining ◽  
Resource Management ◽  
Decision Tree ◽  
Human Resource Management ◽  
Human Resource ◽  
Classification Accuracy ◽  
Ensemble Classifier ◽  
Economic Management ◽  
Mining Technology ◽  
Tree Algorithms

The purposes are to manage human resource data better and explore the association between Human Resource Management (HRM), data mining, and economic management. An Ensemble Classifier-Decision Tree (EC-DT) algorithm is proposed based on the single decision tree algorithm to analyze HRM data. The involved single decision tree algorithms include C4.5, Random Tree, J48, and SimpleCart. Then, an HRM system is established based on the designed algorithm, and the evaluation management and talent recommendation modules are tested. Finally, the designed algorithm is compared and tested. Experimental results suggest that C4.5 provides the highest classification accuracy among the single decision tree algorithms, reaching 76.69%; in contrast, the designed EC-DT algorithm can provide a classification accuracy of 79.97%. The proposed EC-DT algorithm is compared with the Content-based Recommendation Method (CRM) and the Collaborative Filtering Recommendation Method (CFRM), revealing that its Data Mining Recommendation Method (DMRM) can provide the highest accuracy and recall, reaching 35.2% and 41.6%, respectively. Therefore, the data mining-based HRM system can promote and guide enterprises to develop according to quantitative evaluation results. The above results can provide a reference for studying HRM systems based on data mining technology.

Enterprise human resource management based on big data mining technology of internet of things

2021 ◽  
pp. 1-7
Author(s):  
He Ma
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Resource Management ◽  
Decision Tree ◽  
Human Resources ◽  
Human Resource Management ◽  
Human Resource ◽  
Information Technologies ◽  
Mining Technology ◽  
C4.5 Algorithm

Human resource management is an important part of business management. Through a set of scientific and effective management methods, human resource management for all employees and business owners of the enterprise. This research aims to explore how to use a large number of data mining and information technologies to solve the problems existing in the company’s human resource management. This course mainly studies the application of a large number of data mining theories, human resource management theory, the technical background of data mining, data mining process and analysis methods, analysis of their potential relationships, and existing problems, to improve the enterprise human resource management department the decision-making ability provides a reference. First, summarize the characteristics of the human resources market business, and compare the existing data mining algorithms. Here, a C4.5 algorithm in the decision tree algorithm is used to apply the job search information of the company’s recruitment of talents to scientifically analyze the information, during the selection and processing, and then the processing results are given to the C4.5 algorithm, and the corresponding decision tree is obtained. The results of this article show that the use of data mining technology can well solve corporate human resource management problems, such as the wages and benefits of corporate employees. Through big data analysis, it is easy to know that the salary of most doctoral diplomas is about 9,500 yuan, and the most salary for a college diploma is about 3,000 yuan (in remote areas). And according to your diploma, your major and benefits are different. Therefore, the human resources department of the enterprise can better discover talents.

Advanced Statistical Tools for Improving Yield and Reliability

1999 ◽  
Author(s):  
Richard C. Kittler
Keyword(s):  
Data Mining ◽  
Analysis Data ◽  
Neural Nets ◽  
Future Trends ◽  
Statistical Tools ◽  
Mining Technology ◽  
Explanatory Variables ◽  
Data Coverage ◽  
Statistical Approaches ◽  
Adequate Data

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.

Framework and performance analysis of college English testing system based on data mining technology

2021 ◽  
pp. 1-11
Author(s):  
Liu Narengerile ◽  
Li Di ◽  
Keyword(s):  
Data Mining ◽  
Test System ◽  
Test Time ◽  
Testing System ◽  
Test Results ◽  
System Software ◽  
Examination System ◽  
Mining Technology ◽  
College English ◽  
And Performance

At present, the college English testing system has become an indispensable system in many universities. However, the English test system is not highly humanized due to problems such as unreasonable framework structure. This paper combines data mining technology to build a college English test framework. The college English test system software based on data mining mainly realizes the computer program to automatically generate test papers, set the test time to automatically judge the test takers’ test results, and give out results on the spot. The test takers log in to complete the test through the test system software. The examination system software solves the functions of printing test papers, arranging invigilation classrooms, invigilating teachers, invigilating process, collecting test papers, scoring and analyzing test papers in traditional examinations. Finally, this paper analyzes the performance of this paper through experimental research. The research results show that the system constructed in this paper has certain practical effects.

scholarly journals Mechanical equipment fault detection applying data mining technology

2020 ◽  
Vol 1684 ◽  
pp. 012024
Author(s):  
Yiqun Liu ◽  
Xiaogang Wang ◽  
Xiaoyuan Gong ◽  
Hua Mu
Keyword(s):  
Data Mining ◽  
Fault Detection ◽  
Mechanical Equipment ◽  
Mining Technology
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