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Articles de revues sur le sujet « Data Mining Trends and Research Frontiers »

Auteur : Grafiati

Publié le 27 juillet 2024

Mis à jour le 31 juillet 2025

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1

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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Chen, Kai, Xiaoping Lin, Han Wang, et al. "Visualizing the Knowledge Base and Research Hotspot of Public Health Emergency Management: A Science Mapping Analysis-Based Study." Sustainability 14, no. 12 (2022): 7389. http://dx.doi.org/10.3390/su14127389.

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Public health emergency management has been one of the main challenges of social sustainable development since the beginning of the 21st century. Research on public health emergency management is becoming a common focus of scholars. In recent years, the literature associated with public health emergency management has grown rapidly, but few studies have used a bibliometric analysis and visualization approach to conduct deep mining and explore the characteristics of the public health emergency management research field. To better understand the present status and development of public health em

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Klongthong, Worasak, Veera Muangsin, Chupun Gowanit, and Nongnuj Muangsin. "Chitosan Biomedical Applications for the Treatment of Viral Disease: A Data Mining Model Using Bibliometric Predictive Intelligence." Journal of Chemistry 2020 (December 28, 2020): 1–12. http://dx.doi.org/10.1155/2020/6612034.

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Chitosan has attracted increasing attention from researchers in the pharmaceutical and biomedical fields as a potential agent for the prevention and treatment of infectious diseases. However, identifying the development of emerging technologies related to this biopolymer is difficult, especially for newcomers trying to understand the research streams. In this work, we designed and implemented a research process based on a bibliometric predictive intelligence model. Our aim is to glean detailed scientific and technological trends through an analysis of publications that include certain word phr

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Tian, Hua, and Jie Chen. "A bibliometric analysis on global eHealth." DIGITAL HEALTH 8 (January 2022): 205520762210913. http://dx.doi.org/10.1177/20552076221091352.

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Background The current coronavirus disease 2019 pandemic highlights the potential of eHealth. Drawing the knowledge map of eHealth research through data mining and visual analysis technology was helpful to systematically present the research status and future trends of global academic circles. Methods Based on the web of Science Core Collection (SCIE/SSCI) database, using bibliometric theory and visual analysis technology, this work analyzed the global eHealth research publications from 2000 to 2021, and introduced the interdisciplinary characteristics, hot topics and future trends in this fie

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Xiao, Wei, Mingxia Liu, and Xubing Chen. "Research Status and Development Trend of Underground Intelligent Load-Haul-Dump Vehicle—A Comprehensive Review." Applied Sciences 12, no. 18 (2022): 9290. http://dx.doi.org/10.3390/app12189290.

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The underground intelligent load-haul-dump vehicle (LHD) is a product of the deep integration of traditional LHD with information network technology, automatic controlling and artificial intelligence technology. It gathers the functions of environmental perception, autonomous driving and fault diagnosis in one machine and exhibits higher safety and greater efficiency than traditional LHD. Hence, it is a particularly important piece of underground mining equipment for building green, safe and smart mines. Taking the studies about intelligent LHD collected by CNKI and WOS databases from 1980 to

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Liu, Huailan, Rui Zhang, Yufei Liu, and Cunxiang He. "Unveiling Evolutionary Path of Nanogenerator Technology: A Novel Method Based on Sentence-BERT." Nanomaterials 12, no. 12 (2022): 2018. http://dx.doi.org/10.3390/nano12122018.

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In recent years, nanogenerator technology has developed rapidly with the rise of cloud computing, artificial intelligence, and other fields. Therefore, the quick identification of the evolutionary path of nanogenerator technology from a large amount of data attracts much attention. It is of great significance in grasping technical trends and analyzing technical areas of interest. However, there are some limitations in previous studies. On the one hand, previous research on technological evolution has generally utilized bibliometrics, patent analysis, and citations between patents and papers, i

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Shi, Xiaoliang, Xinyue Zhang, Shuaiyu Lu, et al. "Dryland Ecological Restoration Research Dynamics: A Bibliometric Analysis Based on Web of Science Data." Sustainability 14, no. 16 (2022): 9843. http://dx.doi.org/10.3390/su14169843.

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Previous research on ecological restoration mainly includes three fields: water ecology, soil ecology, and atmospheric ecology, and the most abundant is in the field of soil ecology, among which the most abundant is in dryland ecological restoration. Research on dryland ecological restoration is very important in ensuring national food security, ecological security, and preventing a return to poverty. However, the previous research results do not clearly present the interconnection between the huge number of existing dryland ecological restoration studies and do not provide a three-dimensional

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Geng, Chengwei, Fei Xiong, Yong Liu, et al. "Systematic Exploration of the Knowledge Graph on Rock Porosity Structure." Buildings 15, no. 1 (2024): 101. https://doi.org/10.3390/buildings15010101.

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The porosity structure of rocks is an important research topic in fields such as civil engineering, geology, and petroleum engineering, with significant implications for groundwater flow, oil and gas reservoir exploitation, and geological hazard prediction. This paper systematically explores the research progress and knowledge graph construction methods for rock porosity structure, aiming to provide scientific foundations for a multidimensional understanding and application of rock porosity structure. It outlines the basic concepts and classifications of rock porosity, including the definition

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Wang, Yulong, Long Zhang, Guoyan Zhu, et al. "Research Frontiers in Ecological Restoration and Carbon Sequestration in Mining Areas: A Visual Analysis Using VOSviewer." International Journal of Sustainable and Green Energy 13, no. 4 (2024): 90–99. https://doi.org/10.11648/j.ijrse.20241304.13.

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The functioning and progress of modern industrial systems are deeply reliant on mineral resources. While mining offers substantial economic and social gains, it also imposes notable environmental impacts. In the context of global climate change, sustainable mining and ecological restoration in mined areas are increasingly connected to carbon sequestration efforts. Enhancing carbon sink capacity in ecological restoration processes is crucial for achieving carbon neutrality. This study aims to review the current research landscape, identify key research areas, and explore future trends in this f

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郭, 江丽. "Research on Personal Privacy Data Governance: Hot Spots, Trends and Frontiers." Operations Research and Fuzziology 13, no. 04 (2023): 3072–81. http://dx.doi.org/10.12677/orf.2023.134308.

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Vasumathy, M., Ujjwal Agarwal, G. Divyamrutha, Harikumar Pallathadka, and Dolpriya Devi Manoharmayum. "Exploring Data Mining Applications and Techniques: A Comprehensive Research Survey." International Journal of Membrane Science and Technology 10, no. 3 (2023): 2909–19. http://dx.doi.org/10.15379/ijmst.v10i3.2736.

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Every second, huge amount of data is generated and accumulated. This data could possibly be used in forecasting the future. Data mining uses this data and generates valuable information which can be transformed into relevant knowledge. Data mining is a technique of identifying outliers, behaviours, trends of patterns and relationship among huge datasets. It is hugely associated with the skill of decision making. The knowledge on a relevant subject will help in understanding future trends. This survey paper supplies the overview of data mining, the processes involved, the scope it can offer, it

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Zhang, Yun Bo, Dong Wang, and Jiang Wu. "Research of Data Mining Technology under Tourism Information." Applied Mechanics and Materials 687-691 (November 2014): 1206–9. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1206.

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In this paper, a large amount of raw data has been accumulated in Sanya travel system, and uses the data mining techniques to achieve the Sanya tourism management by the tree algorithm and associated computer research technology. Realizate the interestion of tourist visitors in shopping trends analysis, the model used include regression analysis and trend analysis to analyze behavior characteristics and trends of tourists. It provides scientific support about tourism services management and tourism marketing strategy, and played an important role in Sanya tourism information applications.

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Jindal, Rajni, and Malaya Dutta Borah. "A Survey on Educational Data Mining and Research Trends." International Journal of Database Management Systems 5, no. 3 (2013): 53–73. http://dx.doi.org/10.5121/ijdms.2013.5304.

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Gao, Shang, and Mei Mei Li. "Research of Data Graph Mining Based on Telecommunication Customers." Applied Mechanics and Materials 443 (October 2013): 402–6. http://dx.doi.org/10.4028/www.scientific.net/amm.443.402.

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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

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Wu, Jiajing, Dongning Jia, Zhiqiang Wei, and Dou Xin. "Development Trends and Frontiers of Ocean Big Data Research Based on CiteSpace." Water 12, no. 6 (2020): 1560. http://dx.doi.org/10.3390/w12061560.

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Modern socio-economic development and climate prediction depend greatly on the application of ocean big data. With the accelerated development of ocean observation methods and the continuous improvement of the big data science, the challenges of multiple data sources and data diversity have emerged in the ocean field. As a result, the current data magnitude has reached the terabyte scale. Currently, the traditional theoretical foundation and technical methods have their inherent limitations and demerits that cannot satisfied the temporal and spatial attributes of the current ocean big data. Nu

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Bayer, Harun, Mustafa Aksogan, Enes Celik, and Adil Kondiloglu. "Big Data Mining and Business Intelligence Trends." Journal of Asian Business Strategy 7, no. 1 (2017): 23–33. http://dx.doi.org/10.18488/journal.1006/2017.7.1/1006.2.23.33.

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The conventional databases are not capable of coping with the high capacity data due to different forms of these data’s and fast production speed. In this context, The Big Data structure comes into the scene. The Big Data has been stated as the gold of our age by many authorities. Today, large sizes of data can be analyzed and this led to changes in the lives of people, companies, states, and researchers. The companies develop effective and efficient solutions by analyzing large size of data through big data solutions for their strategic decisions, operational processes, campaign management an

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Levkivskyi, Vitalii, Nadiia Lobanchykova, and Dmytro Marchuk. "Research of algorithms of Data Mining." E3S Web of Conferences 166 (2020): 05007. http://dx.doi.org/10.1051/e3sconf/202016605007.

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The article explores data mining algorithms, which based on rules and calculations, that allow us to create a model that analyzes the data provided by searching for specific patterns and trends. The purpose of this work is to analyze correlation-regression algorithms on a statistical dataset of chronic diseases. Data mining allows building many models, multiple algorithms can be used within a single solution. The article explores the algorithms of clustering, correlation analysis, Naive Bayes algorithm for obtaining different views of data. Since diabetes is one of the most dangerous chronic d

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Zelenkov, Yury, and Ekaterina Anisichkina. "Trends in data mining research: A two-decade review using topic analysis." Business Informatics 15, no. 1 (2021): 30–46. http://dx.doi.org/10.17323/2587-814x.2021.1.30.46.

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This work analyses the intellectual structure of data mining as a scientific discipline. To do this, we use topic analysis (namely, latent Dirichlet allocation, DLA) applied to the proceedings of the International Conference on Data Mining (ICDM) for 2001–2019. Using this technique, we identified the nine most significant research flows. For each topic, we analyse the dynamics of its popularity (number of publications) and influence (number of citations). The central topic, which unites all other direction, is General Learning, which includes machine learning algorithms. About 20% of the resea

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Agarwal,, Prakhar, Vinay Pandey, and Dr Bindu Garg. "Survey on Current Trends and Techniques of Data Mining Research." International Journal of Research in Advent Technology 7, no. 4 (2019): 133–37. http://dx.doi.org/10.32622/ijrat.74201920.

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Choi, Jinsu, and Hyewon Chung. "Analysis of Research Trends in Process Data using Text Mining." Journal of Curriculum and Evaluation 27, no. 3 (2024): 197–221. http://dx.doi.org/10.29221/jce.2024.27.3.197.

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Prasad, Bakshi Rohit, and Sonali Agarwal. "Stream Data Mining: Platforms, Algorithms, Performance Evaluators and Research Trends." International Journal of Database Theory and Application 9, no. 9 (2016): 201–18. http://dx.doi.org/10.14257/ijdta.2016.9.9.19.

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Bruno, Francesco, Luigi Palopoli, and Simona E. Rombo. "New Trends in Graph Mining." International Journal of Knowledge Discovery in Bioinformatics 1, no. 1 (2010): 81–99. http://dx.doi.org/10.4018/jkdb.2010100206.

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Searching for repeated features characterizing biological data is fundamental in computational biology. When biological networks are under analysis, the presence of repeated modules across the same network (or several distinct ones) is shown to be very relevant. Indeed, several studies prove that biological networks can be often understood in terms of coalitions of basic repeated building blocks, often referred to as network motifs.This work provides a review of the main techniques proposed for motif extraction from biological networks. In particular, main intrinsic difficulties related to the

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J. Jerom Stuward, S. Sri Gugan, and A. Subhashini. "Data Mining for Literary Trends: A Big Data Approach." Shanlax International Journal of English 12, S1-Dec (2023): 167–73. http://dx.doi.org/10.34293/rtdh.v12is1-dec.90.

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In the rapidly evolving landscape of digital humanities, the exploration of literary trends through the lens of big data and data mining methodologies. Traditional approaches to literary analysis have grappled with the sheer volume of textual data, hindering comprehensive examinations across diverse genres and historical periods. Recognizing the transformative potential of big data, these limitations and provide a scalable framework for the nuanced exploration of literary landscapes. On harnessing the power of data mining to uncover overarching trends, stylometric nuances, and thematic evoluti

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Li, Yue, Zhaoying Li, Chunjie Li, et al. "Out-of-hospital cardiac arrest: A data-driven visualization of collaboration, frontier identification, and future trends." Medicine 102, no. 33 (2023): e34783. http://dx.doi.org/10.1097/md.0000000000034783.

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One of the main causes of death is out-of-hospital cardiac arrest (OHCA), which has a poor prognosis and poor neurological outcomes. This phenomenon has attracted increasing attention. However, there is still no published bibliometric analysis of OHCA. This bibliometric analysis of publications on OHCA aimed to visualize the current status of research, determine the frontiers of research, and identify future trends. Publications on OHCA were downloaded from the web of science database. The data elements included year, countries/territories, institutions, authors, journals, research areas, cita

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Ekwe, Prince O., Mathew Okoronkwo, Tochi P. Ukwome, and Valentine U. Anozie. "Multidimensional Data Analysis, Data Mining and Knowledge Discovery." Multidimensional Data Analysis, Data Mining and Knowledge Discovery 9, no. 1 (2024): 6. https://doi.org/10.5281/zenodo.10656236.

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In recent times, the rate of usage and consumption of data has led to the need for these data to be organized, analyzed and used for futuristic prediction and decision making in order to improve human lives and future prediction in different fields of endeavor. Multidimensional Data Analysis, Data Mining and Knowledge Discovery are all associated with the organization, analysis and extraction of a data set for organization’s decision making and futuristic prediction. In this research work, our focus was on the techniques, application of data mining as well as the phases involved in data

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Miao, Yan, Ying Zhang, and Lihong Yin. "Trends in hepatocellular carcinoma research from 2008 to 2017: a bibliometric analysis." PeerJ 6 (August 15, 2018): e5477. http://dx.doi.org/10.7717/peerj.5477.

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Objectives To comprehensively analyse the global scientific outputs of hepatocellular carcinoma (HCC) research. Methods Data of publications were downloaded from the Web of Science Core Collection. We used CiteSpace IV and Excel 2016 to analyse literature information, including journals, countries/regions, institutes, authors, citation reports and research frontiers. Results Until March 31, 2018, a total of 24,331 papers in HCC research were identified as published between 2008 and 2017. Oncotarget published the most papers. China contributed the most publications and the United States occupie

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Zhou, Ge. "Railway Track Irregularity Data Mining and Time Series Trend Forecasting Research." Applied Mechanics and Materials 666 (October 2014): 272–75. http://dx.doi.org/10.4028/www.scientific.net/amm.666.272.

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Railway track ride is one of the important indicators of the state of the tracks,This article making railway track irregularity data mining,The paper railway track irregularity data mining,data analysis implied regularity and A mathematical model to predict the time-series trends in research,Analysis of the data implied regularity and Build mathematical model getting on time series trend forecasting research.

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Kim, Han-Sol, and Jeong-A. Park. "Analysis of Research Trends in Cosmetology Education using Text Mining." Journal of the Korean Society of Cosmetology 29, no. 3 (2023): 582–92. http://dx.doi.org/10.52660/jksc.2023.29.3.582.

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Identifying academic research trends is an essential task to understand the development patterns of how the discipline has changed in the times and social trends based on the accumulated research results so far, and further establish academic identity. This study was conducted according to the following procedure to analyze the trend of Cosmetology Education Research using text mining. The research procedure proceeded to the stages of data collection, data cleaning, text mining, network analysis, and CONCOR analysis. This study conducted a trend analysis of Cosmetology Education Research using

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Ling, Nie Hui, Chwen Jen Chen, Chee Siong Teh, Dexter Sigan John, Looi Chin Ch’ng, and Yoon Fah Lay. "Global Trends of Educational Data Mining in Online Learning." International Journal of Technology in Education 6, no. 4 (2023): 656–80. http://dx.doi.org/10.46328/ijte.558.

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Educational data mining (EDM) in online learning involves data mining techniques to analyze data from online environments to gain insights into student behavior, performance, and engagement. This study explored EDM in online learning publication trends and focuses. It involved a bibliometric analysis of 615 scholarly works related to EDM in online learning as recorded in Scopus, the largest peer-reviewed citation database, on February 1, 2023. The study examined EDM in online learning publications regarding its evolution and distribution, key focus areas, impact and performance, and prominent

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Du, Xu, Juan Yang, Jui-Long Hung, and Brett Shelton. "Educational data mining: a systematic review of research and emerging trends." Information Discovery and Delivery 48, no. 4 (2020): 225–36. http://dx.doi.org/10.1108/idd-09-2019-0070.

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Purpose Educational data mining (EDM) and learning analytics, which are highly related subjects but have different definitions and focuses, have enabled instructors to obtain a holistic view of student progress and trigger corresponding decision-making. Furthermore, the automation part of EDM is closer to the concept of artificial intelligence. Due to the wide applications of artificial intelligence in assorted fields, the authors are curious about the state-of-art of related applications in Education. Design/methodology/approach This study focused on systematically reviewing 1,219 EDM studies

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Taksa, Isak. "David Taniar: Research and Trends in Data Mining Technologies and Applications." Information Retrieval 11, no. 2 (2008): 165–67. http://dx.doi.org/10.1007/s10791-008-9056-x.

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Das, Subasish, Karen Dixon, Xiaoduan Sun, Anandi Dutta, and Michelle Zupancich. "Trends in Transportation Research." Transportation Research Record: Journal of the Transportation Research Board 2614, no. 1 (2017): 27–38. http://dx.doi.org/10.3141/2614-04.

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Proceedings of journal and conference papers are good sources of big textual data to examine research trends in various branches of science. The contents, usually unstructured in nature, require fast machine-learning algorithms to be deciphered. Exploratory analysis through text mining usually provides the descriptive nature of the contents but lacks quantification of the topics and their correlations. Topic models are algorithms designed to discover the main theme or trend in massive collections of unstructured documents. Through the use of a structural topic model, an extension of latent Dir

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Hao, T., and C. Weng. "Adaptive Semantic Tag Mining from Heterogeneous Clinical Research Texts." Methods of Information in Medicine 54, no. 02 (2015): 164–70. http://dx.doi.org/10.3414/me13-01-0130.

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SummaryObjectives: To develop an adaptive approach to mine frequent semantic tags (FSTs) from heterogeneous clinical research texts.Methods: We develop a “plug-n-play” framework that integrates replaceable un-supervised kernel algorithms with formatting, functional, and utility wrappers for FST mining. Temporal information identification and semantic equivalence detection were two example functional wrappers. We first compared this approach’s recall and efficiency for mining FSTs from ClinicalTrials.gov to that of a recently published tag-mining algorithm. Then we assessed this approach’s adap

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Cho, Nahye, and Youngok Kang. "A Research Trends about Spatio-temporal Data Mining and Visualization of Log Data." Journal of the Korean Cartographic Association 16, no. 3 (2016): 15–27. http://dx.doi.org/10.16879/jkca.2016.16.3.015.

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Wang, Yinying. "Education policy research in the big data era: Methodological frontiers, misconceptions, and challenges." education policy analysis archives 25 (August 28, 2017): 94. http://dx.doi.org/10.14507/epaa.25.3037.

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Despite abundant data and increasing data availability brought by technological advances, there has been very limited education policy studies that have capitalized on big data—characterized by large volume, wide variety, and high velocity. Drawing on the recent progress of using big data in public policy and computational social science research, this commentary discusses how to approach big data and how big data can be used in education policy research. First, I introduce big data that is potentially relevant to education policy research. I then present methodological frontiers by examining

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Li, Baochan, Anan Pongtornkulpanich, and Thitinan Chankoson. "Knowledge Mapping to Understand Corporate Value: Literature Review and Bibliometrics." Journal of Risk and Financial Management 17, no. 2 (2024): 42. http://dx.doi.org/10.3390/jrfm17020042.

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The purpose of this study is to summarize the research results on corporate value published from 2000 to 2022; show the research overview, hot trends, and topic evolution of this research field; provide new ideas for the mining of the research frontiers of corporate value and a summary of the change rules of research hotspots; and describe prospects for the evolution direction and path of future research. Combining the bibliometric research method with a literature review, the research results on corporate value were analyzed quantitatively by querying the WOS database from 2000 to 2022; the a

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Baker, Ryan S.J.d., and Kalina Yacef. "The State of Educational Data Mining in 2009: A Review and Future Visions." Journal of Educational Data Mining 1, no. 1 (2009): 3–17. https://doi.org/10.5281/zenodo.3554658.

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We review the history and current trends in the field of Educational Data Mining (EDM). We consider the methodological profile of research in the early years of EDM, compared to in 2008 and 2009, and discuss trends and shifts in the research conducted by this community. In particular, we discuss the increased emphasis on prediction, the emergence of work using existing models to make scientific discoveries ("discovery with models"), and the reduction in the frequency of relationship mining within the EDM community. We discuss two ways that researchers have attempted to categorize the diversity

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Khin, Sein Hlaing, and Myo Kay Khine Thaw Yin. "Applications, Techniques and Trends of Data Mining and Knowledge Discovery Database." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1604–6. https://doi.org/10.5281/zenodo.3591147.

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Data Mining and Knowledge Discovery is intended to be the best technical publication in the field providing a resource collecting relevant common methods and techniques. Traditionally, data mining and knowledge discovery was performed manually. As time passed, the amount of data in many systems grew to larger than terabyte size, and could no longer be maintained manually. Besides, for the successful existence of any business, discovering underlying patterns in data is considered essential. This paper proposed about applications, techniques and trends of Data Mining and Knowledge Discovery Data

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H., Ramprasanth, and M. Shanmugapriya Dr. "SURVEY ON DATA MINING FOR WEB SEMANTIC ANALYSIS." IJRSET JUNE Volume 9 Issue 6 9, no. 6 (2022): 23–27. https://doi.org/10.5281/zenodo.6793309.

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Data mining plays an important role in various human activities because it extracts the unknown useful patterns (or knowledge). Due to its capabilities, data mining become an essential task in large number of application domains such as banking, retail, medical, insurance, bioinformatics, etc. To take a holistic view of the research trends in the area of data mining, a comprehensive survey is presented in this paper. This paper presents a systematic and comprehensive survey of various data mining tasks and techniques. Further, various real-life applications of data mining are presented in this

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Du, Chunjing, Yi Zhang, Hanwen Zhang, Hua Zhang, Jingyuan Liu, and Ning Shen. "Bibliometric Analysis of Research Trends and Prospective Directions of Lung Microbiome." Pathogens 13, no. 11 (2024): 996. http://dx.doi.org/10.3390/pathogens13110996.

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The lung microbiome has emerged as a pivotal area of research in human health. Despite the increasing number of publications, there is a lack of research that comprehensively and objectively presents the current status of lung microbiome-related studies. Thus, this study aims to address this gap by examining over two decades of publications through bibliometric analysis. The original bibliographic data of this study were obtained from the Web of Science Core Collection, focusing on publications from 2003 to 2023. The analysis included the data extraction and examination of authors, affiliation

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Park, Shinjae. "Trend Analysis of Fluency-Related Research Articles: Using Data Mining." Forum for Linguistic Studies 6, no. 4 (2024): 449–62. http://dx.doi.org/10.30564/fls.v6i4.6797.

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This research seeks to identify recent research trends in analysing fluency in English language studies since 2010. The importance of studying corpora that compile registered research in linguistics is growing; thus, it is time to consider the significance of data mining analysis on the corpora. Considering this emerging research topic, this analysis intends to generate and compare word clouds and word metric charts from speaking and writing abstract corpora in linguistics research centred on the keyword ‘Fluency’. This comparison is minimally addressed in extant research; hence, this investig

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