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.

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

2009 ◽  
pp. 223-229
Author(s):  
Martin Burgard ◽  
Franca Piazza
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Data Warehouse ◽  
Human Resource ◽  
Business Intelligence ◽  
Multidimensional Data ◽  
Business Intelligence Systems ◽  
Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

A Proposed Model for Predicting Employees’ Performance Using Data Mining Techniques

2020 ◽  
Vol 17 (8) ◽  
pp. 3804-3809
Author(s):  
A. Yovan Felix ◽  
Karthik Reddy Vuyyuru ◽  
Viswas Puli
Keyword(s):  
Data Mining ◽  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Human Resource Professionals ◽  
Quality Factors ◽  
Proposed Model ◽  
Opportune Time ◽  
Using Data ◽  
The Impact

Human Resource Management has gotten one of the basic pastimes of supervisors and chiefs in practically wide variety of corporations to include plans for accurately locating profoundly qualified representatives. In similar way, administrations come to be intrigued about the presentation of these representatives. Particularly to guarantee the fitting person apportioned to the beneficial employment on the opportune time. From right here the enthusiasm of statistics in mining process has been growing that its goal is disclosure of facts from huge measures of statistics. Three fundamental Data Mining strategies were applied for building the arrangement version and distinguishing the quality factors that emphatically impact the exhibition. To get a profoundly actual version, a few trials were achieved dependent on the beyond procedures which can be actualized in WEKA tool for empowering leaders and Human Resource professionals to anticipate and improve the exhibition of their representatives. This paper makes use of Hadoop for the remedy of great measure of data with which may be guaranteed to be able to decide the impact.

scholarly journals Effect of Information Technology on the Human

2017 ◽  
Vol 2 (3) ◽  
pp. 98
Author(s):  
Yuhan Guo
Keyword(s):  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Closed Loop ◽  
Modern Human ◽  
Loop Model ◽  
Economic Management ◽  
Technology Application ◽  
Information Technology Application

<p><em>With the continuous development of information technology, information technology has infiltrated the divers industries of society. The impacts of information technology on social economic management including human resource management have become stronger. This paper starts with the essence of information handling and analysis of information technology, explores the influence of information technology on the model of human resource management, and builds the closed loop model modern human resource management supported by information technology. Meanwhile, the paper analyzes the effects of closed loop model of modern human resource management supported by information technology, and the requirements resulting from information technology application in the future to help people to understand human resource management from the perspective of information technology application. </em></p>

Partition Real Data in Decision Tree Using Statistical Criterion

2013 ◽  
Vol 380-384 ◽  
pp. 1469-1472
Author(s):  
Gui Jun Shan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Real Data ◽  
Statistical Criterion ◽  
Partition Scheme ◽  
C4.5 Decision Tree ◽  
Tree Algorithms ◽  
Partition Method

Partition methods for real data play an extremely important role in decision tree algorithms in data mining and machine learning because the decision tree algorithms require that the values of attributes are discrete. In this paper, we propose a novel partition method for real data in decision tree using statistical criterion. This method constructs a statistical criterion to find accurate merging intervals. In addition, we present a heuristic partition algorithm to achieve a desired partition result with the aim to improve the performance of decision tree algorithms. Empirical experiments on UCI real data show that the new algorithm generates a better partition scheme that improves the classification accuracy of C4.5 decision tree than existing algorithms.

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