Enterprise Competitive Intelligence System Research Based on Data Mining Technology

2014 ◽  
Vol 651-653 ◽  
pp. 1562-1565
Author(s):  
Shao Kun
Keyword(s):  
Data Mining ◽  
Rapid Development ◽  
Competitive Intelligence ◽  
Practical Significance ◽  
Reserve Capacity ◽  
Competitive Advantages ◽  
Mining Technology ◽  
Intelligence System ◽  
Data Control ◽  
Competitive Intelligence System

Under the background of current continuous changing market economy and rapid development of information, enterprise will increase the demand of information or data, and correspondingly put forward higher requirements for its quality. For the purpose of better usage and information data control, enterprises put forward pioneering construct competitive intelligence system. Competitive intelligence system can provide accurate, effective and high value, timeliness strong intelligence data to enterprises and the enterprises may adjust their competitive strategy, and effectively enhance their own competitive advantages. The application of data mining technology in enterprise competitive intelligence system can upgrade information reserve capacity and optimize information channels to improve system efficiency effectively. In the process of enterprises development, the establishment of competitive intelligence system based on data mining technology has particularly important practical significance, so we should put more effort into establishing and running the intelligence system.

Design and Implementation of Office Management System in Small and Medium Sized Enterprises Based on Data Mining Technology

2014 ◽  
Vol 686 ◽  
pp. 300-305
Author(s):  
Qiang Fei Yin ◽  
Qiu Li
Keyword(s):  
Data Mining ◽  
Theoretical Foundation ◽  
Competitive Intelligence ◽  
Intelligence Analysis ◽  
Mining Technology ◽  
Fcm Algorithm ◽  
Intelligence System ◽  
Design And Implementation ◽  
Office Management ◽  
Competitive Intelligence System

This paper introduces data mining technology in enterprise competitive intelligence system; and then introduced theoretical foundation and main clustering method of cluster analysis. The article emphasis on the FCM algorithm and principle and described implementation steps, and proposed the improvement FCM algorithm based on K mean particle size; finally, realize the design and implementation of enterprise competitive intelligence analysis and mining service system, and the improved FCM algorithm is applied in the system.

The Enterprise Competitive Intelligence System Based on Data Mining

2014 ◽  
Vol 543-547 ◽  
pp. 4553-4556
Author(s):  
Xiao Guang Li ◽  
Zhan Jun Gao
Keyword(s):  
Data Mining ◽  
Knowledge Economy ◽  
Competitive Intelligence ◽  
General Process ◽  
Intelligence System ◽  
Competitive Intelligence System

In knowledge economy era, data and information become important economy resources. Drawing valuable information quickly from great amount of datum and reacting immediately, will become the key of corporation success. The paper introduces Competitive Intelligence and Corporation Competitive Intelligence System (CIS) firstly. Then, the paper gives the concept of Data Mining and general process. Furthermore, the paper gives a model of Corporation Competitive Intelligence System based on data mining and its relevant process.

scholarly journals Precise Position Intelligent Matching System of Online Recruitment Platform Based on Data Mining Technology

2021 ◽  
Vol 2066 (1) ◽  
pp. 012001
Author(s):  
Zhen Gao
Keyword(s):  
Data Mining ◽  
Rapid Development ◽  
Internet Technology ◽  
Job Seekers ◽  
Mining Technology ◽  
Position Information ◽  
Precise Position ◽  
Random Mass ◽  
Online Recruitment ◽  
Platform System

Abstract With the rapid development of Internet technology and computer technology, network applications have been developed more and more, and have penetrated into all walks of life in society. The emergence of the networking of the talent market has made the scale of online recruitment increase, and the amount of data on the Internet has become larger and larger, and online recruitment has become the main channel for corporate recruitment. Therefore, how to use the massive online recruitment data to quickly and accurately find the corresponding information and explore the hidden knowledge mode is a very valuable research topic. Data mining (DM) is a technology for data analysis for large amounts of data. It can discover hidden, hidden, and potentially useful knowledge hidden in the data from the vague, noisy, and random mass data, and build relevant Model, realize prediction, etc. The characteristics of data mining technology (DMT) are very suitable for the analysis of online recruitment information, research on large amounts of information, and find out the knowledge in it for decision support. This article aims to study the accurate job matching system of the online recruitment platform based on DMT. Based on the analysis of the advantages of online recruitment, related DMT and the design principles of the online recruitment platform system, the data collected by Weka DM tools are analyzed. Analyzing and getting useful job positions is just to provide job seekers and corporate-related recruiters with useful job information. The experimental results show that the online recruitment platform system can complete the collection of online recruitment position information, and can realize the DM function, which has good practical application value.

Research of Data Graph Mining Based on Telecommunication Customers

2013 ◽  
Vol 443 ◽  
pp. 402-406 ◽  
Author(s):  
Shang Gao ◽  
Mei Mei Li
Keyword(s):  
Data Mining ◽  
Graph Mining ◽  
Pattern Mining ◽  
Rapid Development ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Practical Significance ◽  
Research Progress ◽  
Graph Data ◽  
Data Graph

With the rapid development of the number of mobile phone users has accumulated a large number of graph data, graph data mining has gradually become a hot area of research. Traditional data such as clustering, classification, frequent pattern mining gradually extended to the field of graph data mining research. Introduced at this stage graph data mining technology research progress, summarizes the characteristics of the graphical data mining, practical significance, the main problem, and scenarios to discuss and forecast chart data, especially research on uncertain graph data become trends and hot spots.

Big data based research on the management system framework of ideological and political education in colleges and universities

2020 ◽  
pp. 1-10
Author(s):  
Yuejun Xia
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Big Data ◽  
Ideological And Political Education ◽  
Political Education ◽  
Representative Point ◽  
Point Sets ◽  
Mining Technology ◽  
Education Management ◽  
Intelligence System

Artificial intelligence model combined with data mining technology can mine useful data from college ideological and political education management, and conduct process evaluation and teaching management. Therefore, based on the superiority of data mining technology and artificial intelligence system, this paper improves the traditional algorithm and constructs a university ideological and political education management model based on big data artificial intelligence. Moreover, this study uses a local sensitive hash function to generate representative point sets and uses the generated representative point sets for clustering operations. In order to verify the performance of the algorithm model, a control experiment is designed to compare the algorithm of this paper with traditional data mining methods. It can be seen from the research results that the algorithm model constructed in this paper has good performance and can be applied to practice.

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