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Добірка наукової літератури з теми "Data Mining Trends and Research Frontiers"

Автор: Grafiati
Опубліковано: 27 липня 2024
Оновлено: 28 липня 2024

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Статті в журналах з теми "Data Mining Trends and Research Frontiers"

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 (May 21, 2021): 143–53. http://dx.doi.org/10.1108/ijcs-01-2021-0001.

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Анотація:
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 situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords. Findings Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich. Originality/value This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.
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2

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 (July 7, 2024): 62–68. http://dx.doi.org/10.59298/inosras/2024/12.1.62680.

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Анотація:
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 displays. In addition to the significant improvements brought about by artificial intelligence, deep learning, and machine learning, it emphasizes statistical inference, exploratory data analysis, and data pretreatment. One of the main ideas behind this review was to use a systematic literature review approach along with meta-analysis techniques to look for new developments and trends in how data is analyzed in a lot of different areas. The article also addresses how data visualization could improve comprehension and dissemination of the results. In promoting responsible data use and legal rules, it also looks at how analytics affects society, the law, and ethical issues. The evaluation underscores the diverse disciplines that employ data analysis, underscoring the need for interdisciplinary coherence and comprehensible algorithms. Finally, this thorough research offers recommendations for analyzing and determining the boundaries of data analysis, in addition to offering insightful viewpoints and opinions that are helpful for academics, professionals, and decision-makers. If we just stay up to speed with the latest advancements and strive to be more, we can utilize data analysis to its fullest potential for complicated problems and positively impact society. Keywords: Data mining, big data analytics, machine learning, algorithms, and data analysis and statistics
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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 (August 9, 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 citations, the main bodies (country, institution and author) that produce health literacy knowledge as well as their mutual cooperation (collaboration network) are both clarified. Additionally, based on the quantitative framework of cocitation analysis, this paper introduces the interdisciplinary features, development trends and hot topics of the field. Finally, by using burst detection technology in the literature, it further reveals the research frontiers of health literacy. Results The results of the BC measures of the global health literacy research collaboration network show that the United States, Australia and the United Kingdom are the major forces in the current international collaboration network on health literacy. There are still relatively very few transnational collaborations between Eastern and Western research institutions. Collaborations in public environmental occupational health, health care science services, nursing and health policy services have been active in the past five years. Research topics in health literacy research evolve over time, mental health has been the most active research field in recent years. Conclusions A systematic approach is needed to address the challenges of health literacy, and the network framework of cooperation on health literacy at regional, national and global levels should be strengthened to further promote the application of health literacy research. In the future, we anticipate that this research field will expand in two directions, namely, mental health literacy and eHealth literacy, both of which are closely linked to social development and issues. The results of this study provide references for future applied research in health literacy.
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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 (September 1, 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, as well as their cooperation (collaboration network), were extracted, and then both were clarified by the software tools VOSviewer (version 1.6.16) and CiteSpace (version 6.1.R2). Results A total of 1796 publications were retrieved between 2001 and 2021, and 473 relevant publications were included after screening. The results showed an increasing trend in published articles. The United States had the largest number of publications (261/473, 55.18%), followed by China and Canada. The University of California, San Francisco, was the most productive institution. Current research hotspots included the analysis of symptom clusters and symptom management in patients with breast cancer and lung cancer, as well as any advanced cancer and cancer cachexia; fatigue-related symptom clusters and depression-anxiety symptom cluster; and the impacts of symptom clusters on quality of life. The research frontiers included analysis between health-related quality of life and symptom clusters, data mining in symptom clusters, research on the mental health status of cancer patients, and study of the mechanism and biological pathways of symptom clusters. Conclusions The study provides insight into the global research perspective for the scientific progress on cancer symptom clusters, which suggests a growing scientific interest in this field, and more studies are warranted to guide symptom management.
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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 (March 28, 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 in the past five years, a review is made from four main aspects including urban traffic, urban ecology, physical geography, human geography, and the tentative explorations of unsupervised learning, semi-supervised learning and reinforcement learning method in urban studies are unscrambled as well. Finally, the advantages of machine learning methods are summarized, and it􀆳s proposed that the application potential of various machine learning methods in multiple fields and perspectives of urban research should be explored in the future, and the cutting-edge trend of efficient combination of intelligent technology and methods with urban research should be grasped.
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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, the CNIK database and the CiteSpace program are utilized. The findings revealed that the quantity of published literature expanded significantly between 2000 and 2021, with some variations along the way, but that the general growth rate remained consistent. Colleges and universities are the driving force behind academic research in the field of digital museums; research institutes and big museums play a key part in the academic research that is being conducted by digital museums. Cooperation between research institutes, on the other hand, is severely lacking. Furthermore, the advancement of digital technology is an unavoidable byproduct of the efforts to transform the digital museum into a smart museum, as previously said. When it comes to digital museum development in the postepidemic period, the optimization and updating of a user-centered information service platform is the most important step toward long-term success. In order to maintain the richness of Chinese traditional culture while also meeting the expanding cultural requirements of the general public, China’s digital museum research has as its ultimate objective the construction of sustainable digital museums that are appropriate for the country’s national conditions. The findings also demonstrate that the construction of a Chinese Digital Museum is a study issue with distinct Chinese features that has the potential to contribute to the preservation of Chinese cultural heritage, both tangible and intangible. This research gives insights into the following aspects: researchers and practitioners from across the world will work together to promote a better knowledge of the building and growth of the digital museum in China, among other things.
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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 (August 1, 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 divided these topics into layers of digital technology, convergence innovation and digital governance, and we constructed a three-level digital economy research framework. Thereafter, the current hot spots and frontier trends were derived based on the number and growth rate of the literature. Our study revealed that the research on digital technology, which is the basic layer of the digital economy, has waned. The field related to the integration and innovation of digital technology and the real economy was the current research focus, among which the results with respect to “New Business Forms in the Digital Age”, “Circular Economy” and “Gig Economy” were abundant. The problems of the unbalanced development of the digital economy and digital monopoly have strengthened research on digital governance. Furthermore, research on “Regional Digital Economy”, “Chinese Digital Economy” and “Data Management” is in its initial stage and is a potential area of future 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 (January 13, 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, environmental sciences, ecology and engineering; (3) the frontier of field research has shifted from using distributed hydrological models in order to simulate runoff and nonpoint source environmental responses to the coupling of technologies and products that can obtain high-precision, high-resolution data with distributed hydrological models. (4) Affected by climate warming, the melting of glaciers has accelerated, and the spatial distribution of permafrost and water resources have changed, which has caused a non-negligible impact on the hydrological process. Therefore, the development of distributed hydrological models suitable for alpine regions and the response of hydrological processes to climate change have also become important research directions at present.
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Borgman, Christine L. "Whose text, whose mining, and to whose benefit?" Quantitative Science Studies 1, no. 3 (September 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 scholarly communication. The quantity of available text has grown faster than has the quality. Academic dossier systems are among the means to acquire more quality data for mining. Universities, publishers, and private enterprise may be able to mine these data for strategic purposes, however. On the positive front, changes in copyright may allow more data mining. Privacy, intellectual freedom, and access to knowledge are at stake. The next frontier of activism in open access scholarship is control over content for mining as a means to democratize knowledge.
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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 (August 22, 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 development context in the field of artificial intelligence-assisted pathology. The patent citations were extracted from the collected patent data, selected highly cited patents to form a co-occurrence matrix, and built a patent citation network based on the co-occurrence matrix in each period. Text clustering is an unsupervised learning method, an important method in text mining, where similar documents are grouped into clusters. The similarity between documents are determined by calculating the distance between them, and the two documents with the closest distance are combined. The method of text clustering was used to identify the technology frontiers based on the patent citation network, which was according to co-word analysis of the title and abstract of the patents in this field. Results 1704 patents were obtained in the field of artificial intelligence-assisted pathology, which had been currently undergoing three stages, namely the budding period (1992–2000), the development period (2001–2015), and the rapid growth period (2016–2021). There were two technology frontiers in the budding period (1992–2000), namely systems and methods for image data processing in computerized tomography (CT), and immunohistochemistry (IHC), five technology frontiers in the development period (2001–2015), namely spectral analysis methods of biomacromolecules, pathological information system, diagnostic biomarkers, molecular pathology diagnosis, and pathological diagnosis antibody, and six technology frontiers in the rapid growth period (2016–2021), namely digital pathology (DP), deep learning (DL) algorithms—convolutional neural networks (CNN), disease prediction models, computational pathology, pathological image analysis method, and intelligent pathological system. Conclusions Artificial intelligence-assisted pathology was currently in a rapid development period, and computational pathology, DL and other technologies in this period all involved the study of algorithms. Future research hotspots in this field would focus on algorithm improvement and intelligent diagnosis in order to realize the precise diagnosis. The results of this study presented an overview of the characteristics of research status and development trends in the field of artificial intelligence-assisted pathology, which could help readers broaden innovative ideas and discover new technological opportunities, and also served as important indicators for government policymaking.
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Книги з теми "Data Mining Trends and Research Frontiers"

1

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

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2

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

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

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

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Oltramari, Alessandro. New Trends of Research in Ontologies and Lexical Resources: Ideas, Projects, Systems. Berlin, Heidelberg: 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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9

Kumar, Narendra, Vikram Bali, Sunil Kumar Chawla, Kakoli Banerjee, and Sanjay Gour. Industry 4. 0, AI, and Data Science: Research Trends and Challenges. Taylor & Francis Group, 2021.

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

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Частини книг з теми "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, 9–45. Boston, MA: 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, 353–65. Cham: 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, 181–91. Cham: 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, 227–37. Cham: 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, 422–31. Berlin, Heidelberg: 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, 379–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11610113_34.

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Ferrara, Alfio, Corinna Ghirelli, Stefano Montanelli, Eugenio Petrovich, Silvia Salini, and Stefano Verzillo. "Topic-Driven Detection and Analysis of Scholarly Data." In Teaching, Research and Academic Careers, 191–221. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07438-7_8.

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AbstractThe chapter presents a topic mining approach that can used for a scholarly data analysis. The idea here is that research topics can emerge through an analysis of epistemological aspects of scholar publications that are extracted from conventional publication metadata, such as the title, the author-assigned keywords, and the abstract. As a first contribution, we provide a conceptual analysis of research topic profiling according to the peculiar behaviours/trends of a given topic along a considered time interval. As a further contribution, we define a disciplined approach and the related techniques for topic mining based on the use of publication metadata and natural language processing (NLP) tools. The approach can be employed within a variety of topic analysis issues, such as country-oriented and/or field-oriented research analysis tasks that are based on scholarly publications. In this direction, to assess the applicability of the proposed techniques for use in a real scenario, a case study analysis based on two publication datasets (one national and one worldwide) is presented.
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Shen, Y., S. Wu, Z. Xia, X. Wang, and J. Wang. "Bibliometric data mining of machine learning in tunnel engineering research: A publication trends analysis and visualization." In Geotechnical Aspects of Underground Construction in Soft Ground, 301–9. London: CRC Press, 2024. http://dx.doi.org/10.1201/9781003413790-36.

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Han, Jiawei, Micheline Kamber, and Jian Pei. "Data Mining Trends and Research Frontiers." In Data Mining, 585–631. 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, 605–54. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-12-811760-6.00022-9.

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Тези доповідей конференцій з теми "Data Mining Trends and Research Frontiers"

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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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Kaur, Prabhjot, and Neha Dhariwal. "Critical Review on Data Mining in Healthcare Sector." In 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2021. http://dx.doi.org/10.1109/smart52563.2021.9676195.

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Asad, Md, Madhurendra Kumar, Pradeep Kumar Shah, and Arbind Kumar Sinha. "Business Growth Forecast using Saket Data Mining Methodology." In 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2021. http://dx.doi.org/10.1109/smart52563.2021.9676278.

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Manocha, Rahul, Er Prabhjot Kaur, and Er Neha Dhariwal. "Utilization Prediction Technique and Analyze Data Mining Architecture." In 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2022. http://dx.doi.org/10.1109/smart55829.2022.10046834.

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Assiri, Saud, Mansour Alyamani, Abdullah Mansour, Bahjat Fakieh, Sahar Badri, and Amal Babour. "Current Shipment Tracking Technologies and Trends in Research." In ICISDM 2020: 2020 the 4th International Conference on Information System and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3404663.3404683.

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