Paper Info
Title
A Comparative Study of Clustering Techniques Applied on Covid-19 Scientific Literature
Abstract
Due to the current emergency situation, caused by COVID-19, the scientific literature on the topic has rapidly grown. At the same time, purposeful and targeted research plans with strong background knowledge is urgently needed. However, the huge number of documents produced by multiple communities generates a fragmented terminology that may cause confusion in information retrieval. To this aim, in a comparative study, we test different techniques to efficiently cluster these publications for improving their level of findability.
Year
DOI
Venue
2020
10.1109/IOTSMS52051.2020.9340213
2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS)
Keywords
DocType
ISBN
Text Embedding,Document Clustering,COVID_19,Machine Learning
Conference
978-1-6654-1926-0
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Valerio Bellandi111.05
Paolo Ceravolo225244.89
Samira Maghool310.71
Stefano Siccardi401.35