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Artykuły w czasopismach na temat „Data mining management”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Duque, Jorge. "Data Mining for Knowledge Management." Procedia Computer Science 239 (2024): 257–64. http://dx.doi.org/10.1016/j.procs.2024.06.170.

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Ozimek, John. "Data Mining." Journal of Database Marketing & Customer Strategy Management 10, no. 3 (2003): 280–81. http://dx.doi.org/10.1057/palgrave.jdm.3240117.

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Shawe-Taylor, J., T. De Bie, and N. Cristianini. "Data mining, data fusion and information management." IEE Proceedings - Intelligent Transport Systems 153, no. 3 (2006): 221. http://dx.doi.org/10.1049/ip-its:20060006.

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Alkadi, Ihssan. "Data Mining." Review of Business Information Systems (RBIS) 12, no. 1 (2008): 17–24. http://dx.doi.org/10.19030/rbis.v12i1.4394.

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Recently data mining has become more popular in the information industry. It is due to the availability of huge amounts of data. Industry needs turning such data into useful information and knowledge. This information and knowledge can be used in many applications ranging from business management, production control, and market analysis, to engineering design and science exploration. Database and information technology have been evolving systematically from primitive file processing systems to sophisticated and powerful databases systems. The research and development in database systems has le

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5

Lai, Maotao. "Smart Financial Management System Based on Data Ming and Man-Machine Management." Wireless Communications and Mobile Computing 2022 (January 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/2717982.

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To begin, the architecture of an intelligent financial management system is thoroughly investigated, and a new architecture of an intelligent financial management support system based on data mining is developed. Second, it goes over the definition and structure of a data warehouse and data mining, as well as how to use data mining strategy and technology in financial management. Data mining in relation to technology is being investigated, as is the development of an intelligent data mining algorithm. The flaws of the intelligent data mining algorithm are discovered through an analysis and sum

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6

Zhang, Jingjing, and Yang Chi. "Data Management and Service Mode of Library Based on Data Mining Algorithm." Scientific Programming 2022 (September 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/2414830.

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Data management for large-scale data library services with mining procedures improves the availability and readiness of heterogeneous sources. The heterogeneous data sources are assimilated as a single entity through mining procedures to meet the data demands. This article introduces connectivity-persistent data mining method (CDMM) to improve the data handling precision with boosting availability. The proposed method relies on federated learning for identifying the service demands, thereby providing data mining. The learning paradigm accumulates information on shared data library existence ov

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7

Bahadir, Cuneyt, and Adem Karahoca. "Airline revenue management via data mining." Global Journal of Information Technology: Emerging Technologies 7, no. 3 (2017): 128–48. http://dx.doi.org/10.18844/gjit.v7i3.2834.

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Revenue maximisation has been of paramount interest in the airline industry during the past few decades, and numerous studies have been reported, aiming at robust analyses. Principal analysis techniques in most of these studies include computational-based prediction algorithms that are used for a given dataset. In this study, airline specific data, which consists of cabin class passenger data, cabin class supplied capacity data, distance of flights, season, year –month data and revenue data, are analysed using various prediction algorithms. Consistencies and accuracies of different algorithms

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8

Padmasini, C. S., and K. Shyamala. "Data Mining in Automotive Customer Management." International Journal of Data Mining Techniques and Applications 5, no. 1 (2016): 35–38. http://dx.doi.org/10.20894/ijdmta.102.005.001.008.

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Leveridge, Michael. "Mining the data on UTUC management." Canadian Urological Association Journal 6, no. 6 (2012): 463. http://dx.doi.org/10.5489/cuaj.142.

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Schumaker, Robert P., Osama K. Solieman, and Hsinchun Chen. "Sports knowledge management and data mining." Annual Review of Information Science and Technology 44, no. 1 (2010): 115–57. http://dx.doi.org/10.1002/aris.2010.1440440110.

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Duque, Jorge, Firmino Silva, and António Godinho. "Data Mining applied to Knowledge Management." Procedia Computer Science 219 (2023): 455–61. http://dx.doi.org/10.1016/j.procs.2023.01.312.

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12

Midoun, Mohammed, and Hafida Belbachir. "A new process for mining spatial databases: combining spatial data mining and visual data mining." International Journal of Business Information Systems 39, no. 1 (2022): 17. http://dx.doi.org/10.1504/ijbis.2022.120366.

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Belbachir, Hafida, and Mohammed Midoun. "A new process for mining spatial databases: combining spatial data mining and visual data mining." International Journal of Business Information Systems 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijbis.2020.10024978.

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14

Priyanka, Saini. "Data Mining Application in Advertisement Management of Higher Educational Institutes." Data Mining Application in Advertisement Management of Higher Educational Institutes 01, apr (2014): 01–11. https://doi.org/10.5281/zenodo.1436264.

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In recent years, Indian higher educational institute’s competition grows rapidly for attracting students to get enrollment in their institutes. To attract students educational institutes select a best advertisement method. There are different advertisements available in the market but a selection of them is very difficult for institutes. This paper is helpful for institutes to select a best advertisement medium using some data mining methods

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15

Balamurugan, S., and Dr M. Selvalakshmi. "Customer Relationship Management Using Data Mining Model." Restaurant Business 118, no. 7 (2019): 95–100. http://dx.doi.org/10.26643/rb.v118i7.7668.

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The paper describes marketing insights from Data Mining about new promotions to create, focus on profitability and emphasis on the most profitable promotion that could be sent. The paper shows about the development of predictive modeling, from data mining which provides insights into future customer behavior and customer profitability. Data Mining provides a blueprint and how to define and use customer profile. It shows how to acquire new customers in the most profitable way possible and retain profitable customers. Data mining is an effective method to target at risk-customers with the right

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16

Koslowsky, S. "Data mining or data moaning?" Journal of Database Marketing & Customer Strategy Management 8, no. 3 (2001): 262–72. http://dx.doi.org/10.1057/palgrave.jdm.3240042.

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17

Liu, Chang Wei, Xin Hong Zhang, Jing Hui Duan, and Bing Zhi Huang. "Customs Risk Management Based on Outlier Data Mining." Applied Mechanics and Materials 501-504 (January 2014): 2682–85. http://dx.doi.org/10.4028/www.scientific.net/amm.501-504.2682.

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By using outlier data mining, this paper studied customs risk management in China. Our research contents are summarized as follows: First, we study the status of Customs risk management and we study other scholars’ research of the outlier data mining. Second, we introduce index of outliers and we use outlier data mining to establish a risk estimation model. Finally, we test the affectivity of this model by empirical analysis.

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18

Zhang, Zhi Ming, Ya Juan Sun, Tai Yu Liu, and Lei Xu. "Data Mining and its Application in Modern Animal Husbandry." Advanced Materials Research 926-930 (May 2014): 2533–36. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2533.

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Modern animal husbandry information technology, networking and intelligent development of data mining techniques proposed requirements. This paper analyzes the data mining for the importance of modern animal husbandry management, data warehouse, data mining technology, focusing on the analysis of data mining in the mining process in the modern animal husbandry management decisions and implementation of technology, pointed out the data mining technology provide more powerful support functions for modern animal husbandry management decisions.

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19

Ouyang, Zhengbin, Yaochenxi Xu, and Xin Xie. "Business management data analysis method based on data mining." Advances in Economics and Management Research 12, no. 1 (2024): 1084. https://doi.org/10.56028/aemr.12.1.1084.2024.

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In China's social and economic development, the business management as the basic component, not only affects the practical economic development goal, but also decides the main direction of enterprise reform and innovation in the new period. Only the orderly development of business management can guarantee our country's economic development environment become more stable. Understanding the current situation of business administration in China, we can see that although the overall level is getting higher and higher, there are still many problems, especially after entering the era of big data, ho

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20

Chen, Zhengxin. "Towards Integrated Study of Data Management and Data Mining." Procedia Computer Science 55 (2015): 1331–39. http://dx.doi.org/10.1016/j.procs.2015.07.117.

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21

Anil, Kumar Tiwari, Ramakrishna G., Kumar Sharma Lokesh, and Kumar Kashyap Sunil. "Academic performance prediction algorithm based on fuzzy data mining." International Journal of Artificial Intelligence (IJ-AI) 8, no. 1 (2019): 26–32. https://doi.org/10.11591/ijai.v8.i1.pp26-32.

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This paper presents an algorithm for prediction of academic performance of students by fuzzy data mining. The fuzzy-trace concept applied to predict the academic performance of the students. An algorithm is proposed in this paper lies with this idea. The fuzzy academic set is generated from the student’s academic data. This is analyzed by the fuzzy-matrix set. The prediction academic data is referred as the management of data or data mining. Data mining is the science of analyzing the data for obtaining more information than the current information. The hidden information appears by this

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22

Dhika, Harry, Fitriana Destiawati, Surajiyo, and Musa Jaya. "Data Mining Approach for Learning management system." IOP Conference Series: Materials Science and Engineering 1088, no. 1 (2021): 012013. http://dx.doi.org/10.1088/1757-899x/1088/1/012013.

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23

Rodpysh, Keyvan Vahidy. "Applying Data Mining in Customer Relationship Management." International Journal of Information Technology, Control and Automation 2, no. 3 (2012): 15–25. http://dx.doi.org/10.5121/ijitca.2012.2302.

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24

Karimipour, F. "Water Quality Management Using GIS Data Mining." Journal of Environmental Informatics 5, no. 2 (2005): 61–71. http://dx.doi.org/10.3808/jei.200500047.

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25

Hovakimyan, Anna, and Siranush Sargsyan. "Data Mining Methods in Educational Process Management." WSEAS TRANSACTIONS ON ADVANCES in ENGINEERING EDUCATION 21 (November 6, 2024): 110–16. http://dx.doi.org/10.37394/232010.2024.21.13.

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The paper addresses the challenge of effectively managing the educational process by leveraging intelligent data analysis of student performance during learning activities. It introduces an approach centered around data clustering, specifically applied to the study of programming disciplines and languages. By utilizing clustering techniques, the paper aims to identify the most challenging topics within a given academic subject, track students’ learning paths, evaluate and enhance teaching methodologies, and create personalized learning plans tailored to individual students’ needs. This approac

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26

Shaw, Michael J., Chandrasekar Subramaniam, Gek Woo Tan, and Michael E. Welge. "Knowledge management and data mining for marketing." Decision Support Systems 31, no. 1 (2001): 127–37. http://dx.doi.org/10.1016/s0167-9236(00)00123-8.

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27

Qi, Chong-chong. "Big data management in the mining industry." International Journal of Minerals, Metallurgy and Materials 27, no. 2 (2020): 131–39. http://dx.doi.org/10.1007/s12613-019-1937-z.

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28

Rygielski, Chris, Jyun-Cheng Wang, and David C. Yen. "Data mining techniques for customer relationship management." Technology in Society 24, no. 4 (2002): 483–502. http://dx.doi.org/10.1016/s0160-791x(02)00038-6.

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29

Guo, Feng, and Huilin Qin. "Data Mining Techniques for Customer Relationship Management." Journal of Physics: Conference Series 910 (October 2017): 012021. http://dx.doi.org/10.1088/1742-6596/910/1/012021.

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Hung, Shin-Yuan, David C. Yen, and Hsiu-Yu Wang. "Applying data mining to telecom churn management." Expert Systems with Applications 31, no. 3 (2006): 515–24. http://dx.doi.org/10.1016/j.eswa.2005.09.080.

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31

Shankar, Venkatesh, and Russell S. Winer. "When customer relationship management meets data mining." Journal of Interactive Marketing 20, no. 3-4 (2006): 2–4. http://dx.doi.org/10.1002/dir.20062.

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32

Hand, D. J. "Mining medical data." Statistical Methods in Medical Research 9, no. 4 (2000): 305–7. http://dx.doi.org/10.1191/096228000701555172.

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Wu, Xiang Min, Cheng Lin Zhao, and Pan Cao. "Research of Data Base and Data Mining in CRM." Applied Mechanics and Materials 543-547 (March 2014): 2988–91. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2988.

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Customer Relationship Management (CRM) is becoming the focus of enterprise and an active research field of computer science. The ariticle introduces some basic concepts about CRM and data mining, and some benefits brought by data mining in CRM. At the end it points out how to apply data mining applications in CRM.

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34

Dastyar, Bagher, Hanieh Kazemnejad, Alireza Asgari Sereshgi, and Mohammad Amin Jabalameli. "Using Data Mining Techniques to Develop Knowledge Management in Organizations: A Review." Journal of Engineering, Project, and Production Management 7, no. 2 (2017): 80–89. http://dx.doi.org/10.32738/jeppm.201707.0004.

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Arora, Siddharth, Parneet Kaur, and Prachi Arora. "Economical Maintenance and Replacement Decision Making in Fleet Management using Data Mining." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 01, no. 02 (2013): 09–20. http://dx.doi.org/10.9756/sijcsea/v1i2/0102580102.

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36

Al-Ali, Romel, Sabri Mekimah, Rahma Zighed, et al. "Evolution and gaps in data mining research: Identifying the bibliometric landscape of data mining in management." Decision Science Letters 14, no. 2 (2025): 435–48. https://doi.org/10.5267/j.dsl.2024.12.011.

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This study conducts a bibliometric analysis of data mining publications in the Scopus database, examining the evolution of the field from 2015 to 2024. The study examines the bibliometric structure of data mining in management. Analyzing 2,942 publications, the research identifies significant growth in data mining studies. It reveals gaps in integrating data mining with decision-making, artificial intelligence, forecasting, and sentiment analysis. Despite a large number of publications, interdisciplinary applications of data mining are limited. The scientific publication on data mining and its

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37

Guo, Yu Dong. "Prototype System of Knowledge Management Based on Data Mining." Applied Mechanics and Materials 411-414 (September 2013): 251–54. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.251.

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Knowledge is a very crucial resource to promote economic development and society progress which includes facts, information, descriptions, or skills acquired through experience or education. With knowledge has being increasingly prominent, knowledge management has become important measure for the core competences promotion of a corporation. The paper begins with knowledge managements definition, and studies the process of knowledge discovery from databases (KDD),data mining techniques and SECI(Socialization, Externalization, Combination, Internalization) model of knowledge dimensions. Finally,

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38

Zekić-Sušac, Marijana, and Adela Has. "Data Mining as Support to Knowledge Management in Marketing." Business Systems Research Journal 6, no. 2 (2015): 18–30. http://dx.doi.org/10.1515/bsrj-2015-0008.

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Abstract Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers’ profiles in freque

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39

Abdullahi, Akibu Mahmoud, Mokhairi Makhtar, and Suhailan Safie. "The patterns of accessing learning management system among students." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (2019): 15–21. https://doi.org/10.11591/ijeecs.v13.i1.pp15-21.

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Learning Management System (LMS) is an online software that was hosted on a server and designed specifically to manage learners’ information, course registration, learning content, and assessment tool. Educational data mining is a way of evaluating and using methods for examining the unique and large dataset that come from educational field, and applying those in order to understand how students learn and the settings in which they learn. Many students use to miss some of the activities posted by their instructors, due to the short deadline, and they are not accessing the LMS regularly o

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40

Combi, C., A. Tucker, and N. Peek. "Biomedical Data Mining." Methods of Information in Medicine 48, no. 03 (2009): 225–28. http://dx.doi.org/10.1055/s-0038-1625129.

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Summary Objective: To introduce the special topic of Methods of Information in Medicine on data mining in biomedicine, with selected papers from two workshops on Intelligent Data Analysis in bioMedicine (IDAMAP) held in Verona (2006) and Amsterdam (2007). Methods: Defining the field of biomedical data mining. Characterizing current developments and challenges for researchers in the field. Reporting on current and future activities of IMIA’s working group on Intelligent Data Analysis and Data Mining. Describing the content of the selected papers in this special topic. Results and Conclusions: I

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41

Shushant Kumar and Thushar Shukla. "Optimizing Supply Chain Management through Advanced Data Mining Applications." International Journal of Sciences and Innovation Engineering 1, no. 3 (2024): 1–9. https://doi.org/10.70849/ijsci27936.

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In the era of globalization and digital transformation, Supply Chain Management (SCM) has become increasingly complex due to the exponential growth of data generated from various sources. Advanced data mining applications have emerged as pivotal tools for optimizing SCM by extracting actionable insights from vast datasets. This paper explores the integration of sophisticated data mining techniques into SCM processes to enhance decision-making, improve operational efficiency, and increase overall competitiveness. Through an extensive literature review and empirical analysis, the study highlight

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42

Kause, Wehelmina Lodia, Zulkifli Djamaluddin Umar, and Abu Hassan Sangaji. "Tata kelola data dan informasi pertambangan di Provinsi Nusa Tenggara Timur." FLOBAMORA 3, no. 1 (2020): 65–74. http://dx.doi.org/10.46888/flobamora.v3i1.46.

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This is an evaluative study that focuses on mining data and information system management. The approach used is a qualitative approach that describes mining system management in East Nusa Tenggara based on mining data and information system management. The results of the study show that mining system management in NTT has not produced data, information that is well-structured, comprehensive, and intact and obtained separately from various sources. On the other hand, mining data and information system management in the NTT Province in the Ministry of Energy and Mineral Resources is precisely sy

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43

Karasözen, Bülent, Alexander Rubinov, and Gerhard-Wilhelm Weber. "Optimization in Data Mining." European Journal of Operational Research 173, no. 3 (2006): 701–4. http://dx.doi.org/10.1016/j.ejor.2005.10.005.

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Bai, Mei. "The Application of Data Mining Technology in the Remote Open Management System." Applied Mechanics and Materials 687-691 (November 2014): 1141–44. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1141.

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This paper introduces the concept of database and data mining, combined with management system of quality assessment system and method of data mining technology. In this paper, applying the data mining skill to the field of remote open management system, introduces the development of data mining in China and the necessity and importance of data mining in remote open information management system. This thesis analyzes the main problems in the remote open management system. On the basis of the relevant researches both at home and abroad, it presents the significance of the application of data mi

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45

Olson, David L. "Data mining in business services." Service Business 1, no. 3 (2006): 181–93. http://dx.doi.org/10.1007/s11628-006-0014-7.

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Tsumoto, Shusaku, and Shoji Hirano. "Risk Mining in Medicine: Application of Data Mining to Medical Risk Management." Fundamenta Informaticae 98, no. 1 (2010): 107–21. http://dx.doi.org/10.3233/fi-2010-219.

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Gurusubramani, S., S. K. Mouleeswaran, Porandla Srinivas, and R. Aruna. "A Data Centre Configurable Data Mining Document Management Information System." Journal of Physics: Conference Series 1964, no. 4 (2021): 042095. http://dx.doi.org/10.1088/1742-6596/1964/4/042095.

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Vararuk, A., I. Petrounias, and V. Kodogiannis. "Data mining techniques for HIV/AIDS data management in Thailand." Journal of Enterprise Information Management 21, no. 1 (2007): 52–70. http://dx.doi.org/10.1108/17410390810842255.

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Majeed, Dilovan Asaad, Hawar Bahzad Ahmad, Ahmed Alaa Hani, et al. "DATA ANALYSIS AND MACHINE LEARNING APPLICATIONS IN ENVIRONMENTAL MANAGEMENT." Jurnal Ilmiah Ilmu Terapan Universitas Jambi 8, no. 2 (2024): 398–408. http://dx.doi.org/10.22437/jiituj.v8i2.32769.

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The rapid expansion of data on air contaminants and climate change, particularly concerning public health, presents both opportunities and challenges for traditional epidemiological methods. This study aims to address these challenges by exploring advanced data collection, pattern identification, and predictive modeling techniques in the context of air pollution research. The focus is leveraging data mining and computational methods to enhance the understanding of air pollution's impact on public health, specifically ozone exposure. A comprehensive review of the scientific literature was condu

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Zhang, Wen Qing. "Application Research of Data Mining Technology on Growth Management of Forestry." Advanced Materials Research 846-847 (November 2013): 995–98. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.995.

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A large number of data have been accumulated in our country during the long-term investigation and statistics of forestry resources, and it has become key problem to find out the relationship of the environment and forest growth from the large number of existing forestry resources data. In this paper, the data mining technology is used in planning and design of forestry resource, and the process of data mining is studied, considering data mining and design process of forestry resource decision, we firstly study data mining technology, then collect the data and perform data processing, select t

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