Clustering research group website homepages

Kenekayoro, Patrick; Buckley, Kevan; Thelwall, Mike

doi:10.1007/s11192-014-1497-y

Cited by 9 publications

(3 citation statements)

References 53 publications

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“…Due to the unreliable quality of hyperlink, some indicators using content analysis, such as web mentions and co-words (co-occurrences), were proposed to measure the visibility of institutions [52]. The inter-department research similarity was detected through co-words in the homepages of 76 computer science departments in the UK and the results show the research similarity between department websites in computer science [53]. Another survey on the collection of commercial websites also argues that co-words analysis potentially can be used to cluster organisations [54].…”

Section: Related Workmentioning

confidence: 99%

Measuring visibility of disciplines on Chinese academic web

Yang

Sun

Huang

2020

Journal of Information Science

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This study proposes a hierarchy affiliation model (department–school–university) to build network between web entities taking into account the domain names, the topological structure of academic network and the disciplinary characteristics of schools and universities synthetically. The study of the Chinese academic web based on the model shows that at the school level, 68 of 95 disciplines (71.6%) are identified from the directed school network and 71 from the undirected school network, respectively; at the university level, four out of seven broad disciplines are found. Furthermore, according to the comparative result based on three types of relations (hyperlinks, citations and collaborations) among universities, we would like to argue with cautions that the structure on academic web would potentially be more suitable to trace the interests in common between institutions.

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Section: Related Workmentioning

confidence: 99%

Measuring visibility of disciplines on Chinese academic web

Yang

Sun

Huang

2020

Journal of Information Science

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“…Competition pressure is a special type of pressure which is mainly generated in the competition environment. Sports competition itself is very challenging, so it is inevitable for athletes to be in a state of stress for a long time[1]. General psychology and sport psychology research shows that competition pressure will give individual athletes and sports organizations to bring many negative effects, such as to cause anxiety, aggressive behavior, low satisfaction, and thus affect the competition results and health of body and mind.…”

Section: Introductionmentioning

confidence: 99%

Cognitive neural mechanism of sports competition pressure source

Zhou

2019

Translational Neuroscience

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At sports events, the athletes by the pressure source is varied, based on the stress status of athletes, many athletes stress related experts at home and abroad to design questionnaire, questionnaire and sports events for athletes with often life process of in-depth and meticulous investigation, has formed the one whole set athletes pressure source of cognitive neuroscience assessment system, sports competition for athlete’s "escort". By participating in state general administration of sports scientific research project " management system of athlete competition pressure cognitive neuroscience" the development of using psychological pressure on athletes’ source data, the application of natural language processing and machine learning technology research these data, mainly using clustering algorithm and recommendation algorithm, thus forming pressure source research results are applied in sports competitions.

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“…Therefore, how to realize such platform to obtain users' interested information by using data mining and machine learning methods is discussed in this paper. [1,2,3,4] At present, analysis in this area is largely focused on papers written in English. International famous citation analysis tools such as DBLP [5], Microsoft Academic Search [6], Google Scholar are most frequently used.…”

Section: Introductionmentioning

confidence: 99%

A Collaborative Filtering Algorithm based on Citation Information

Bai¹,

Wang²,

Huang³

et al. 2015

Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

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Objective: With the rapid growing number of published scientific papers in the age of big data, users often find themselves difficult to select useful information from such massive academic information. This paper aims at the problems of collaborative filtering techniques in scientific citation data. Methods: This paper proposes an improved machine learning algorithm, that is designed to predict user ratings of academic theses by using Fisher Linear Regression combined with information of confidence scores, preference scores, the number of cooperative users in science citation data and the actual citation scores. Results: Multiple features considered in the algorithm have a positive impact on the recommendation results. Conclusion: This paper proposed a collaborative filtering recommendation algorithm combined with citation data analysis in order to improve the accuracy of predicted results. The experiments have shown that the proposed algorithm is proved to be effective and improve the accuracy of recommendation.

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Clustering research group website homepages

Cited by 9 publications

References 53 publications

Measuring visibility of disciplines on Chinese academic web

Measuring visibility of disciplines on Chinese academic web

Cognitive neural mechanism of sports competition pressure source

A Collaborative Filtering Algorithm based on Citation Information

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