scholarly journals Application of Data Mining Technology Based on Data Center

2022 ◽  
Vol 2146 (1) ◽  
pp. 012017
Author(s):  
Longjun Zhang ◽  
Kun Liu ◽  
Ilyar Ilham ◽  
Jiaxin Fan
Keyword(s):  
Data Mining ◽  
Computer Science ◽  
Data Center ◽  
Rapid Development ◽  
Research Field ◽  
The Internet ◽  
Test Results ◽  
Mining Technology ◽  
Processing Data ◽  
Definition Of

Abstract Data mining technology refers to the use of mathematics, statistics, computer science and other methods to process a large amount of information to obtain useful conclusions and provide valuable decisions for people. With the rapid development and popularization of the Internet era and the more and more extensive application of computers in various fields, data mining technology has become a hot research field in today’s society. Based on the data center, this paper studies the data mining technology. Firstly, this paper expounds the definition of data mining, and studies the process of data mining and the steps of processing data. Then, this paper also designs and studies the framework of data mining, and tests the performance of the algorithm. Finally, the test results show that data mining technology can well meet the target requirements.

Research and Application in the Web Data Mining Based on the XML

2014 ◽  
Vol 644-650 ◽  
pp. 2124-2127
Author(s):  
Fen Liu
Keyword(s):  
Data Mining ◽  
Information Transmission ◽  
Rapid Development ◽  
Data Sources ◽  
The Internet ◽  
Web Data ◽  
Web Data Mining ◽  
Mining Technology ◽  
Web Documents ◽  
The Web

With the rapid development of Internet, the Internet has become the important resources of information transmission and share. The characteristics of Web data are semi-structured, heterogeneous and mass, making traditional data mining technology indirectly applied to Web data sources. Web data mining refers to extracting a potential, useful model from the Web documents or Web activities. Because of the structural and expansibility of XML, research on XML combined with Web data mining has also became popular.

scholarly journals ANALISIS DATA MINING HOTEL BOOKING MENGGUNAKAN MODEL ID3

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Agus Budiyantara ◽  
Andreanus Kevin Wijaya ◽  
Anthony Gunawan ◽  
Michael Rolland
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Success Rate ◽  
Rapid Development ◽  
The Internet ◽  
Processing Data ◽  
The Future ◽  
Hotel Booking ◽  
Business Sectors

<em>The rapid development of information technology in this era makes it easier for someone to get information. Many business sectors are now promoting their products or services on the internet. An example is a hotel, in the technological era now we can easily find out about hotel information, ranging from location, price, and others. With the convenience to get information about this hotel, customers are indirectly increasing in a hotel. This of course causes the data contained in a hotel to increase as well. These data can be processed until we get an output and there is also data that is missing or cannot be processed. The data that can be processed can be analyzed until finally it becomes an information and prediction. In this journal, we will explain the Data Mining analysis in a hotel to analyze the success rate of a hotel. By doing this analysis, you will get insights about the level of success of the hotel and can also predict the future. Thus later the results of this analysis can be used by the hotel to assist in better decision making. Processing data in this study using the Rapid Miner application by entering data of customers who make hotel reservations</em>

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 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.

Privacy Preserving Data Mining, Concepts, Techniques, and Evaluation Methodologies

2008 ◽  
pp. 2379-2401 ◽  
Author(s):  
Igor Nai Fovino
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Privacy Preserving ◽  
Research Field ◽  
Sensitive Information ◽  
Discovery Process ◽  
Privacy Preserving Data Mining ◽  
Mining Technology ◽  
Evaluation Methodologies ◽  
New Research

Intense work in the area of data mining technology and in its applications to several domains has resulted in the development of a large variety of techniques and tools able to automatically and intelligently transform large amounts of data in knowledge relevant to users. However, as with other kinds of useful technologies, the knowledge discovery process can be misused. It can be used, for example, by malicious subjects in order to reconstruct sensitive information for which they do not have an explicit access authorization. This type of “attack” cannot easily be detected, because, usually, the data used to guess the protected information, is freely accessible. For this reason, many research efforts have been recently devoted to addressing the problem of privacy preserving in data mining. The mission of this chapter is therefore to introduce the reader in this new research field and to provide the proper instruments (in term of concepts, techniques and example) in order to allow a critical comprehension of the advantages, the limitations and the open issues of the Privacy Preserving Data Mining Techniques.

Privacy Preserving Data Mining, Concepts, Techniques, and Evaluation Methodologies

2008 ◽  
pp. 277-301
Author(s):  
Igor Nai Fovino
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Privacy Preserving ◽  
Research Field ◽  
Sensitive Information ◽  
Discovery Process ◽  
Privacy Preserving Data Mining ◽  
Mining Technology ◽  
Evaluation Methodologies ◽  
New Research

Intense work in the area of data mining technology and in its applications to several domains has resulted in the development of a large variety of techniques and tools able to automatically and intelligently transform large amounts of data in knowledge relevant to users. However, as with other kinds of useful technologies, the knowledge discovery process can be misused. It can be used, for example, by malicious subjects in order to reconstruct sensitive information for which they do not have an explicit access authorization. This type of “attack” cannot easily be detected, because, usually, the data used to guess the protected information, is freely accessible. For this reason, many research efforts have been recently devoted to addressing the problem of privacy preserving in data mining. The mission of this chapter is therefore to introduce the reader in this new research field and to provide the proper instruments (in term of concepts, techniques and example) in order to allow a critical comprehension of the advantages, the limitations and the open issues of the Privacy Preserving Data Mining Techniques.

Design of College English Process Evaluation System Based on Data Mining Technology and Internet of Things

2020 ◽  
Vol 16 (2) ◽  
pp. 18-33 ◽  
Author(s):  
Hongli Lou
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Process Evaluation ◽  
Evaluation System ◽  
Current Situation ◽  
The Internet ◽  
Mining Technology ◽  
College English ◽  
Procedural Evaluation ◽  
The Internet Of Things

This article proposes a new idea for the current situation of procedural evaluation of college English based on Internet of Things. The Internet of Things is used to obtain the intelligent data to enhance the teaching flexibility. The data generated during the process of procedural evaluation is carefully analyzed through data mining to infer whether the teacher's procedural evaluation in English teaching can be satisfied.

Data Mining Utilization for Plant Historical Processing Accumulated Data

2013 ◽  
Vol 411-414 ◽  
pp. 1040-1043
Author(s):  
Qing Li ◽  
Bao Liang Ge ◽  
Jie Liu ◽  
Yan Xiong Fu
Keyword(s):  
Data Mining ◽  
Positive Relationship ◽  
Copper Recovery ◽  
Mining Technology ◽  
Copper Concentrate ◽  
Processing Data ◽  
Total Metal ◽  
Plant Processing ◽  
Concentrate Grade ◽  
Relationship Of

A large amount of processing data was accumulated within plant processing. And its necessary to use this data for plant processing as well as its administration. In this article the data mining technology and its utilization were discussed, according to research results, the fitting relationship is:Cu recovery (%)= -1.1221*lime dosage (Kg/t)+92.6, the lime dosage alteration effect on copper recovery are 1.41% absolutely and 1.66% relatively. The fitting relationship of copper concentrate grade and lime dosage is:Cu grade (%)= 0.0554*lime dosage (Kg/t)+19.271, the lime dosage alteration effect on copper recovery are 0.070% absolutely and 0.36% relatively. It can be concluded that the lime dosage has a great effect on copper recovery, and lime dosage is relative to the total metal minerals in the ore, because the fitting relationship of the lime dosage and metal minerals summation in the ore is:Dosage (Kg/t)= 0.0487*total metal minerals (%)+2.6441, the lime dosage show a positive relationship with total metal minerals in the ore.

Study on Data Mining Techniques and Algorithms of Association Rules Data Mining

2014 ◽  
Vol 543-547 ◽  
pp. 2040-2044
Author(s):  
Yan Bo Wang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Granular Computing ◽  
Rapid Development ◽  
System Structure ◽  
Data Set ◽  
Mining Technology ◽  
Technology System ◽  
Operation Process ◽  
Database Technology

With the rapid development of network and database technology, data need to be processed massively increased, how to carry out effective data mining is a serious problem. The mature development of granular computing algorithm provides new ideas and new methods to study for data mining. Association rules of granular computing can reduce the number of object scanning data set, and improve the efficiency of the algorithm. In this paper we introduce the data source, classification, technology, system structure, operation process, application in other areas of data mining technology. Based on association rules of granular computing, data mining technology can provide quantitative basis for enterprise in screening assessment, so the service object has a stronger competitive advantage and focus more on its problems.

Analysising on the Library Services in the Age of Big Data

2014 ◽  
Vol 1044-1045 ◽  
pp. 1066-1070
Author(s):  
Chen Wei ◽  
Xiao Di Wang ◽  
Ran Ma ◽  
Bing Qi Wang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Social Networking ◽  
Data Storage ◽  
Rapid Development ◽  
Structured Data ◽  
The Internet ◽  
Library Services ◽  
Library Service

The advent of the age of big data brings not only the rapid development of the Internet, scientific research, social networking and other fields, but also help and challenges to the application of library. For example, the library service applications in data storage, data mining, data analysis, etc. can identify hidden values behind the data only through systematic organization and analysis of massive structured, unstructured, and semi-structured data, ​​in order to predict the future development of library and promote its better development.

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