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Gotowe bibliografie tematyczne / Data Mining Trends and Research Frontiers

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Gotowa bibliografia na temat „Data Mining Trends and Research Frontiers”

Autor: Grafiati

Data publikacji: 27 lipca 2024

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Data Mining Trends and Research Frontiers"

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Huang, Yue, Hu Liu, and Jing Pan. "Identification of data mining research frontier based on conference papers." International Journal of Crowd Science 5, no. 2 (2021): 143–53. http://dx.doi.org/10.1108/ijcs-01-2021-0001.

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Purpose Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers. Design/methodology/approach This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual s

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Nnachi, Alum Benedict, Echegu Darlington Arinze, and Aleke Jude Uchechukwu. "Exploring the Frontiers of Data Analysis: A Comprehensive Review." INOSR APPLIED SCIENCES 12, no. 1 (2024): 62–68. http://dx.doi.org/10.59298/inosras/2024/12.1.62680.

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The process of assessing, cleansing, transforming, and interpreting data to find trends, patterns, or insights that might guide choices and help manage problems is known as data analysis. Data analysis is a leading light on the cutting edge of contemporary research, revealing the path of knowledge across many areas. It includes the methodical examination of data to find trends, patterns, and insights that are helpful for the analytical and creative processes. This review also examines how data analysis is developing, emphasizing new approaches, paradigms, viewpoints, and graphical data display

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Qi, Shaojie, Fengrui Hua, Shengyuan Xu, Zheng Zhou, and Feng Liu. "Trends of global health literacy research (1995–2020): Analysis of mapping knowledge domains based on citation data mining." PLOS ONE 16, no. 8 (2021): e0254988. http://dx.doi.org/10.1371/journal.pone.0254988.

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Background During uncertainties associated with the COVID-19 pandemic, effectively improving people’s health literacy is more important than ever. Drawing knowledge maps of health literacy research through data mining and visualized measurement technology helps systematically present the research status and development trends in global academic circles. Methods This paper uses CiteSpace to carry out a metric analysis of 9,492 health literacy papers included in Web of Science through mapping knowledge domains. First, based on the production theory of scientific knowledge and the data mining of

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An, Ran, Yuan Luo, Wen-Feng Chen, Muhammad Sohaib, and Mei-Zi Liu. "Global trends and knowledge-relationship of symptom clusters in cancer research: a bibliometric analysis over the past 20 years." Frontiers of Nursing 10, no. 3 (2023): 273–88. http://dx.doi.org/10.2478/fon-2023-0031.

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Abstract Objective To use CiteSpace and VOSviewer to investigate the scientific production in the field of symptom clusters in cancer research. Methods The search was performed using the terms “symptom clusters,” “cancer,” and “oncology” on the Web of Science Core Collection database. The retrieval time was from 2001 to 2021, which covers the last 2 decades. Based on the production theory of scientific knowledge and the data mining of citations, data pertaining to the annual publications, journals, countries, organizations, authors, and keywords that produce symptom clusters in cancer research

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ZENG, Wenjing, Kai ZHOU, and Yiqun XIONG. "Advances and Review of Machine Learning Applications in Urban Studies from 2005 to 2020." Chinese Geography Sciences Review 1, no. 1 (2023): 16–30. http://dx.doi.org/10.48014/cgsr.20220711001.

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Machine learning, as a new method for data mining and problem prediction, has been widely used in various fields of urban studies in recent years, which requires a periodical summary of relevant literature. Start with data types, selection and preprocessing, this paper introduces the characteristics and applicability of various machine learning algorithms, and analyzes the cross-fields, hot spots, frontiers and trends of machine learning and urban studies from 2005 to 2020 by using Citespace. Second, focusing on the application of supervised machine learning algorithms from relevant literature

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Hou, Yajing, Lijun Xu, and Lu Chen. "Hotspots and Cutting-Edge Visual Analysis of Digital Museum in China Using Data Mining Technology." Computational Intelligence and Neuroscience 2022 (May 25, 2022): 1–16. http://dx.doi.org/10.1155/2022/7702098.

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During the last several years, the building and development of digital museums has grown in importance as a study issue of increasing importance. On the other hand, systematic and extensive literature study on digital museums is rare in the academic community throughout the world. This paper employs data mining technology to conduct a comprehensive analysis of the total amount of academic literature, research hotspots, frontiers, and trends in the field of digital museums in China since the beginning of the twenty-first century, including both historical and contemporary data. In this research

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Qin, Fangling, Ying Zhu, Tianqi Ao, and Ting Chen. "The Development Trend and Research Frontiers of Distributed Hydrological Models—Visual Bibliometric Analysis Based on Citespace." Water 13, no. 2 (2021): 174. http://dx.doi.org/10.3390/w13020174.

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Based on the bibliometric and data visualization analysis software Citespace, this study carried out document statistics and information mining on the Web of Science database and characterized the distributed hydrological model knowledge system from 1986 to 2019. The results show a few things: (1) from 1986 to 2019, the United States and China accounted for 41% of the total amount of publications, and they were the main force in the field of distributed hydrological model research; (2) field research involves multiple disciplines, mainly covering water resources, geology, earth sciences, envir

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Zhang, Changlu, Qiong Yang, Jian Zhang, Liming Gou, and Haojie Fan. "Topic Mining and Future Trend Exploration in Digital Economy Research." Information 14, no. 8 (2023): 432. http://dx.doi.org/10.3390/info14080432.

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This work proposes a new literature topic clustering analysis framework, based on which the topics of digital-economy-related studies are condensed. First, we calculated the word vector of keywords using the FastText model, and then the keywords were merged according to semantic similarity. A hierarchical clustering method based on the Jaccard coefficient was employed to cluster the domain documents. Finally, the information gain method was applied to estimate the high-gain feature words for each category of topics. Based on the above framework, 23 categories of research topics were formed. We

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Borgman, Christine L. "Whose text, whose mining, and to whose benefit?" Quantitative Science Studies 1, no. 3 (2020): 993–1000. http://dx.doi.org/10.1162/qss_a_00053.

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Scholarly content has become more difficult to find as information retrieval has devolved from bespoke systems that exploit disciplinary ontologies to keyword search on generic search engines. In parallel, more scholarly content is available through open access mechanisms. These trends have failed to converge in ways that would facilitate text data mining, both for information retrieval and as a research method for the quantitative social sciences. Scholarly content has become open to read without becoming open to mine, due both to constraints by publishers and to lack of attention in scholarl

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Zhang, Ting, Juan Chen, Yan Lu, Xiaoyi Yang, and Zhaolian Ouyang. "Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network." PLOS ONE 17, no. 8 (2022): e0273355. http://dx.doi.org/10.1371/journal.pone.0273355.

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Objectives This paper aimed to identify the technology frontiers of artificial intelligence-assisted pathology based on patent citation network. Methods Patents related to artificial intelligence-assisted pathology were searched and collected from the Derwent Innovation Index (DII), which were imported into Derwent Data Analyzer (DDA, Clarivate Derwent, New York, NY, USA) for authority control, and imported into the freely available computer program Ucinet 6 for drawing the patent citation network. The patent citation network according to the citation relationship could describe the technology

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Książki na temat "Data Mining Trends and Research Frontiers"

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David, Taniar, ed. Research and trends in data mining technologies and applications. Idea Group Pub., 2007.

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1949-, Körner Christian, and Spehn E. M, eds. Data mining for global trends in mountain biodiversity. Taylor & Francis, 2010.

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Ting, I.-Hsien. Social network mining, analysis, and research trends: Techniques and applications. Information Science Reference, 2012.

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M, Spehn E., and Körner Christian 1949-, eds. Data mining for global trends in mountain biodiversity. CRC Press, 2010.

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Oltramari, Alessandro. New Trends of Research in Ontologies and Lexical Resources: Ideas, Projects, Systems. Springer Berlin Heidelberg, 2013.

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“Data Mining Concepts & Techniques”. 3rd ed. Morgan Kaufmann Publishers, 2011.

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Taniar, David. Research and Trends in Data Mining Technologies and Applications (Advances in Data Warehousing and Mining Series) (Advanced Topics in Data Warehousing and Mining). IGI Global, 2007.

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Taniar, David. Research And Trends in Data Mining Technologies And Applications (Advances in Data Warehousing and Mining Series) (Advances in Data Warehousing and Mining ... in Data Warehousing and Mining Series). Idea Group Publishing, 2007.

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Industry 4.0, AI, and Data Science: Research Trends and Challenges. CRC Press LLC, 2021.

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Industry 4. 0, AI, and Data Science: Research Trends and Challenges. Taylor & Francis Group, 2021.

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Części książek na temat "Data Mining Trends and Research Frontiers"

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Deogun, Jitender S., Vijay V. Raghavan, Amartya Sarkar, and Hayri Sever. "Data Mining: Trends in Research and Development." In Rough Sets and Data Mining. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4613-1461-5_2.

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Li, Zili, and Li Zeng. "Research Hotspots and Trends in Data Mining: From 1993 to 2016." In Data Mining and Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61845-6_36.

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Harris, Trevor M. "Exploratory Spatial Data Analysis: Tight Coupling Data and Space, Spatial Data Mining, and Hypothesis Generation." In Regional Research Frontiers - Vol. 2. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50590-9_11.

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Tianyuan, Zhang, and Sérgio Moro. "Research Trends in Customer Churn Prediction: A Data Mining Approach." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72657-7_22.

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Janusz, Andrzej, Hung Son Nguyen, Dominik Ślęzak, Sebastian Stawicki, and Adam Krasuski. "JRS’2012 Data Mining Competition: Topical Classification of Biomedical Research Papers." In Rough Sets and Current Trends in Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32115-3_50.

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Zhang, Cong-Le, Sheng Huang, Gui-Rong Xue, and Yong Yu. "Image Description Mining and Hierarchical Clustering on Data Records Using HR-Tree." In Frontiers of WWW Research and Development - APWeb 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11610113_34.

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Han, Jiawei, Micheline Kamber, and Jian Pei. "Data Mining Trends and Research Frontiers." In Data Mining. Elsevier, 2012. http://dx.doi.org/10.1016/b978-0-12-381479-1.00013-7.

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Han, Jiawei, Jian Pei, and Hanghang Tong. "Data mining trends and research frontiers." In Data Mining. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-12-811760-6.00022-9.

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Tartan, Emre Öner. "PINAR: Population INfograms for Analysis and Research." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. https://doi.org/10.3233/faia241451.

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This study introduces PINAR (Population INfograms for Analysis and Research), a Streamlit-based web tool for visualizing and analyzing demographic data sourced from the Turkish Statistical Institute (TURKSTAT) using data mining techniques. PINAR integrates various demographic modules, including birth rates, population distribution by marital status, sex, and age groups as well as the most common baby names, and the most prevalent names and surnames over the years. A key feature of the tool is its ability to generate infograms which visualize population data on a map of Turkey, providing insigh

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Xiong, Huayong. "Research on Personalized Recommendation System of New Media Short Video Based on Big Data Mining." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230857.

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In the era of mobile internet, the new media video entertainment industry has developed rapidly, and entertainment information is showing an explosive growth trend. The problems of information overload and knowledge redundancy are becoming increasingly severe. How to efficiently and accurately obtain interesting entertainment information has become a difficult problem in people’s daily lives. Recommendation systems are one of the important applications of data mining and an important way to solve information overload. The recommendation system mainly includes login registration module, persona

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Streszczenia konferencji na temat "Data Mining Trends and Research Frontiers"

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Shi, Weichao, Rong Xue, Maorong Hu, and QI Li. "Research progress and trends in machining surface reconstruction based on CiteSpace." In 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), edited by Xin Zhang and Minghao Yin. SPIE, 2024. http://dx.doi.org/10.1117/12.3048404.

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Singh, Navjot, and Gaurav Bathla. "A Review on Opinion Mining Approaches: Using Deep Learning for Large Scale Data." In 2024 13th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2024. https://doi.org/10.1109/smart63812.2024.10882566.

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Verma, Brijesh Kumar, Nidhi Srivastava, and Ajay Kumar Bharti. "Advanced Models for Predicting Student Performance in Higher Education using Machine Learning and Data Mining Techniques." In 2024 13th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2024. https://doi.org/10.1109/smart63812.2024.10882496.

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Hao, Liu, Chen Yunwei, and Zhang Biao. "Study on the Differences Between Journal Papers and Conference Papers in the Frontier of Basic Research: Taking the Terahertz Field as an Example." In 20th International Conference on Scientometrics & Informetrics. Institute for Informatics and Automation Problems of NAS RA, 2025. https://doi.org/10.51408/issi2025_042.

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Analyzing the discrepancies in the content of journal papers and conference papers in the frontier of basic research is beneficial for a comprehensive understanding of the characteristics and patterns of basic research development. Retrieve data from the Web of Science (WoS) database in the frontier of basic research. Employ a comprehensive approach using bibliometrics, social network analysis, and text mining methods to compare the differences in content between journal papers and conference papers.Explore aspects such as publication trends, paper contributions, and thematic evolution to anal

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Ritika, Ritika, and Sunil Gupta. "Research Frontiers in Sequential Pattern Mining." In Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India. EAI, 2022. http://dx.doi.org/10.4108/eai.16-4-2022.2318239.

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Yang, ShaoChen, and RuiQi Wang. "Research of stock trends based on data mining." In 2021 2nd International Conference on Big Data and Informatization Education (ICBDIE). IEEE, 2021. http://dx.doi.org/10.1109/icbdie52740.2021.00029.

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Dubey, Shish Kumar, Sunil Gupta, Shri Krishna Pandey, and Sonu Mittal. "Ethical Considerations in Data Mining and Database Research." In 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2024. http://dx.doi.org/10.1109/icrito61523.2024.10522200.

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Mishra, Brojo Kishore, Deepannita Hazra, Kahkashan Tarannum, and Manas Kumar. "Business Intelligence using Data Mining techniques and Business Analytics." In 2016 International Conference System Modeling & Advancement in Research Trends (SMART). IEEE, 2016. http://dx.doi.org/10.1109/sysmart.2016.7894496.

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Cuzzocrea, Alfredo. "Scalable Joins over Big Data Streams: Actual and Future Research Trends." In 2022 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2022. http://dx.doi.org/10.1109/icdmw58026.2022.00132.

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Goel, Shubhi, R. K. Dwivedi, and Anu Sharma. "Analysis of Social Network using Data Mining Techniques." In 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART). IEEE, 2020. http://dx.doi.org/10.1109/smart50582.2020.9337153.

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