Deep Analysis of Data Mining Method in Personalized Information System of University Library

2014 ◽  
Vol 8 (1) ◽  
pp. 772-776
Author(s):  
Kunpeng Wang
Keyword(s):  
Data Mining ◽  
Information System ◽  
Clustering Algorithm ◽  
Information Service ◽  
Analysis Data ◽  
Student Interest ◽  
Time Data ◽  
Mining Technology ◽  
University Library ◽  
System Establishment

In order to discuss the application method and execution process of data mining in personalized information system establishment of university library, the thesis introduces existing condition of university library and insufficiency of the information service system. At the same time, data mining technology is introduced to simply describe the data mining process and introduce two top applications of the data mining technology in personalized library information system, namely student interest guidance quality and establishment of relevancy rule. Furthermore, more classical algorithms (FP-growth algorithm and K-mean clustering algorithm) are introduced in the data mining technology in detail. The data mining technology is a new data processing method. Nowadays, as for high flux reactor data analysis, data mining technology becomes more and more important in the construction process of personalized library information system.

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.

Early Warning of Power Plant Equipment Based on Massive Real-Time Data Mining Technology

2014 ◽  
Vol 599-601 ◽  
pp. 1487-1490 ◽  
Author(s):  
Li Kun Zheng ◽  
Kun Feng ◽  
Xiao Qing Xiao ◽  
Wei Qiao Song
Keyword(s):  
Data Mining ◽  
Power Plant ◽  
Real Time ◽  
Early Warning ◽  
Time Data ◽  
Mining Technology ◽  
Equipment Safety ◽  
State Evaluation ◽  
Power Plant Equipment ◽  
Real Time Data

This paper mainly discusses the application of the mass real-time data mining technology in equipment safety state evaluation in the power plant and the realization of the equipment comprehensive quantitative assessment and early warning of potential failure by mining analysis and modeling massive amounts of real-time data the power equipment. In addition to the foundational technology introduced in this paper, the technology is also verified by the application case in the power supply side remote diagnosis center of Guangdong electric institute.

scholarly journals Analysis of Characteristics of Tennis Singles Matches Based on 5G and Data Mining Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ming Li ◽  
Qinsheng Li ◽  
Yuening Li ◽  
Yunkun Cui ◽  
Xiufeng Zhao ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Statistical Methods ◽  
Analysis Data ◽  
Scientific Basis ◽  
Experimental Simulation ◽  
Mining Technology ◽  
Offensive Tactics ◽  
Analysis System ◽  
Scientific Decision

The level of technical and tactical decision-making in a tennis game has a very important impact on the outcome of the game. How to discover the characteristics and rules of the game from a large amount of technical and tactical data, how to overcome the shortcomings of traditional statistical methods, and how to provide a scientific basis for correct decision-making are a top priority. Based on 5G and association analysis data mining theory, we established a data mining model for tennis technical offensive tactics and association rules and conducted specific case studies. It can calculate the maximization and distribution rate of certain technologies, also distinguish between the athlete’s gain and loss rate and the spatial position on the track, and use artificial statistical methods to cause errors and subjective participation. This solution provides objective and scientific decision support for this problem and is used in the decision-making of the landing point in tennis match technology and tactics. Experimental simulation shows that the data mining technology analysis system used for regional tennis matches is more concise, efficient, and accurate than traditional movie analysis methods.

scholarly journals The Usage of Data Mining Technology in Financial Information System: An Application on Borsa Istanbul

2016 ◽  
Vol 3 (1) ◽  
pp. 51
Author(s):  
Ceren Uzar
Keyword(s):  
Data Mining ◽  
Information System ◽  
Human Resources ◽  
New Technologies ◽  
Financial Information ◽  
Primary Data ◽  
Survey Method ◽  
Mining Technology ◽  
Past Data ◽  
Level Of Understanding

<div><p>Data mining technology is one of the new technologies that have become increasingly popular. Data mining enables to form forecasts and models regarding future by making use of past data. It can be costly, risky and time consuming for enterprises to gain knowledge. Firms gain important competitive advantage by data mining methods. This study analyzes on the readiness to implement and the extent of utilization of data mining technologies in the Financial Information Systems (FIS) in Borsa Istanbul and also researches the level of understanding of, perceptions of and readiness to implement data mining technologies within the Borsa Istanbul.  Analysis was undertaken using SPSS. Manufacturing and financial enterprises are the universal of this study. Primary data were obtained by using survey method and questionnaire technique and findings of the study were evaulated. Technological, organisational and human resources issues had a significant role in the decision to, or not to utilize data mining technology. The ability to use data mining technology was found to be increased the performance of the Financial Information System.<strong></strong></p></div>

Data Mining technology as a tool for supporting analytical decision making process in Health Information Management System (HIMS)

2021 ◽  
Vol 5 (2) ◽  
pp. 139-147
Author(s):  
Gbenga Femi Asere ◽  
Dung Emmanuel Botson
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information System ◽  
Strategic Management ◽  
Information Management ◽  
Management System ◽  
Clinical Decision Making ◽  
Information Management System ◽  
Decision Making Process ◽  
Mining Technology

Wide spread use of information system in the delivery of managed healthcare system and the challenges of identifying and disseminating relevant healthcare information, complex and diverse data and knowledge forms and tasks coupled with the prevalence of legacy systems require automated approaches for effective and efficient utilization of massive amount of data to support in strategic planning and decision-making and assist the strategic management mechanisms. Despite the fact that data mining is progressively used in information systems as a technology to support analytical decision making, it is however still barely used in hospital information system to support analytical decision making process. Hence, this paper presents the usefulness of data mining technology in Hospital Information Management System (HIMS). Data mining technology offered capabilities to increase the productivity of medical personnel, analyze care outcomes, lower healthcare costs, improve healthcare quality by using fast and better clinical decision making and generally assist the strategic management mechanisms.

The Research of the Distributed Resource-AwareK-means Clustering Algorithm

2015 ◽  
Vol 19 (3) ◽  
pp. 343-348 ◽  
Author(s):  
Xiaoni Wang ◽  
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Single Point ◽  
Data Communication ◽  
The Internet ◽  
Mining Method ◽  
Time Data ◽  
Network Bandwidth ◽  
Data Mining Method ◽  
Battery Energy

According to the characteristics of the constrained resource in distributed real-time data mining in the Internet of Things (IOT) environment, a distributed data mining method is researched in such environment. Based on the limits of computing ability, storage ability, battery energy resources, network bandwidth, and the Internet single point failure, the distributed network data mining method is researched, and the adaptive technology and peer-to-peer node method are adopted. The DRA-Kmeans algorithm of data mining based on theK-means algorithm is proposed, and the amount of data communication among the sites to reduce the number of iterations and clustering is reduced. Clustering efficiency is improved, and better clustering results and execution efficiency are achieved.

scholarly journals Optimization of Clinical Nursing Management System Based on Data Mining

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yongxia Chen
Keyword(s):  
Data Mining ◽  
Information System ◽  
Information Management ◽  
Management System ◽  
Clinical Care ◽  
Nursing Management ◽  
Mining Technology ◽  
Clinical Nursing ◽  
Medical Order ◽  
Nursing Information System

The clinical nursing work based on the establishment and improvement of the clinical nursing system breaks through the traditional nursing work model, which has achieved the advantages of full traceability, practical operation, comprehensive analysis, and individual error correction of nursing work, and greatly improves the nursing quality and work efficiency of nurses. With the advent of the era of big data, how to organically combine data mining technology with nursing information to optimize the nursing information system, apply big data to clinical nursing work through nursing information system, and provide patients with more efficient, high-quality, and safe nursing services is a problem that needs urgent consideration in today’s era. Therefore, this research is based on the framework of the hospital’s existing clinical care system, using data mining technology to improve the Bayesian algorithm and data preprocessing, optimizes the design of functional modules in the clinical nursing management system, and optimizes the patient information management, medical order management, medical order execution management, basic information and expense management, nursing execution process management, system and data management, barcode management, physical sign management, WAP information management, and other subsystems in the clinical nursing information management system. Experiments have proved that the use of a data mining-based clinical care management system can simplify user operations and improve users’ application of software. The application system of nursing methods based on data mining technology more completely integrates nursing information management business, makes nursing information management initially “digital,” and can improve the quality of hospital care to a large extent.

Target Values of Combustion Optimization in Coal Fire Boiler Based on Data Mining

2012 ◽  
Vol 608-609 ◽  
pp. 1143-1146
Author(s):  
Wen Huan Wang ◽  
Wei Guo Pan ◽  
Ming Fu He ◽  
Bing Chao Pan ◽  
Yi Qiong Pan ◽  
...  
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Parameter Range ◽  
Mining Technology ◽  
Target Value ◽  
Coal Fire ◽  
Target Values ◽  
Combustion Optimization

In this paper, K-means clustering algorithm of data mining technology is applied to determine the targeted value. The historical operation data of 600MW unit boiler are studied to determine the boiler combustion optimization target values of different conditions. The normalized method is used for handling data, directly reflects the position of operation target value in the whole parameter range, provides guide for boiler combustion optimization.

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