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| Ranking Method of Topic Popularity Based on Skyline Algorithm |
| Wan Xiaoji1,2, Li Hailin1, Gong Yanyan1, Lin Hailong1 |
1.College of Business Administration, Huaqiao University, Quanzhou 362021
2.Business Management Research Center, Huaqiao University, Quanzhou 362021 |
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Abstract Aiming at the objectivity of existing topics sorting and the complexity and difficulty of high-dimensional data processing, a topic ranking method based on affinity propagation clustering and skyline algorithm is proposed. On the basis of the importance of keywords and affinity propagation clustering algorithm, the initial core topics of literature are adaptively obtained. The average similarity coefficient is used to further perform secondary affinity propagation clustering for the original topic clusters. Taking the average citations and downloads of the center, typically keywords, as the index of the topic popularity, the skyline collection is achieved by employing the skyline algorithm. Finally, the topic ranking result is achieved scientifically through principal component analysis. Through data processing and mining of the supply chain literature in China National Knowledge Infrastructure (CNKI) from 2010 to 2020, it is found that the proposed new method can effectively identify the research topics and their ranking, which can not only provide the related researchers with advice and guidance in scientific topic selection, but also offer the relevant journals decision-making support of the precise selection.
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Received: 21 July 2021
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