A bibliometric analysis on Data Mining and Big Data

Tseng, Shu-Feng; Won, Yu Ling; Yang, Jiann Min

doi:10.1504/ijeb.2016.075333

Cited by 4 publications

(2 citation statements)

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“…Some useful research was done especially related to the Bibliometrics specialized in big data (Singh et al 2015;Mishra et al 2016;Sivarajah et al 2017). Also, some other majors too got use of the big data like; industry 4.0 (Muhuri et al, 2019), IOT publishing (Nobre & Tavares 2017), technological maps (Gerdsri et al, 2013), cloud computing (Heilig & Voß 2014), IT (Khaparde & Pawar, 2013), data mining or a big one (Tseng et al, 2016), and other fields like cybersecurity and machine learning (Makawana & Jhaveri 2018).…”

Section: Related Studiesmentioning

confidence: 99%

The Role of Big Data and Machine Learning in COVID-19

Ababneh¹,

Aljarrah²,

Karagozlu³

2020

BRAIN

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The big rise in the existence of digital data contributed to creating many good chances, especially related to corporations, institutions and firms. Also, it gives the capability to scrimp data regarding its major or area, where the countries have benefited from the analysis of big data (BD) greatly in the face of epidemics and diseases, especially COVID-19 since BD is now available everywhere around us, from official reports and scientific studies related to virology and epidemiology. The general aim of this study is to clarify how the conjunction among both BD and machine learning (ML) created huge differences in data science and a big influence on the applications related to a lot of fields chiefly in COVID-19. The method which is used in this study 'relevance tree' by identifying papers related to ML and BD, especially in COVID-19. The results have been shown that the use of reinforcement learning in analyzing BD provides effective and tremendous results, although it faces many challenges and restrictions that have been explained in detail in this study. In addition, the results showed that most of the countries in the time of Corona turned into smart cities, totally dependent on smart applications based on the analysis of BD using ML, and one of the most important applications that were circulated around the world global positioning system. In addition to the results that have been found, data privacy is one of the most important challenges facing data analysis. Consequently, it recommended future researchers to focus on studying the challenges faced by ML in analyzing medical data in the COVID-19 era.

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Section: Related Studiesmentioning

confidence: 99%

The Role of Big Data and Machine Learning in COVID-19

Ababneh¹,

Aljarrah²,

Karagozlu³

2020

BRAIN

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“…Bibliometric studies related to data mining are also widely applied, such as in text mining [48], data mining algorithms [49], big data analytics [50]- [53], validation models [54], and natural language process (NLP) [55]. Silva et al [56] also carried out a bibliometric study related to association rules to preserve the privacy of confidential information.…”

mentioning

confidence: 99%

Bibliometric study of association rule-market basket analysis

Yanti,

Elquthb,

Rachmadewi

et al. 2024

IJAAS

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Association rule-market basket analysis (AR-MBA) is a data mining technique for finding distinguished relationship patterns from a collection of items. The application of AR-MBA is also increasingly widespread, starting from retail and hotels to hospitals. So, bibliometrics related to AR-MBA needs to be done to reveal what research opportunities can be later carried out by reviewing and analyzing publications about AR-MBA. 91 bibliographies in 1 decade from 2012-2022 were collected using Harzing's Publish or Perish (PoP). VOSviewer is also employed to map authorship and publication topic trends. This paper is innovative because it identifies trends and future research directions in data mining, specifically in association with AR-MBA. The findings show publication productivity, top authors, types of publications, annual topic trends within a decade, term distribution, most cited and most influential articles, and research gaps that can be opportunities for further research.

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