Multi-view clustering with exemplars for scientific mapping

Xiangfeng Meng, Xinhai Liu (), YunHai Tong, Wolfgang Glänzel and Shaohua Tan
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Xiangfeng Meng: Peking University
Xinhai Liu: People’s Bank of China
YunHai Tong: Peking University
Wolfgang Glänzel: Katholieke Universiteit Leuven
Shaohua Tan: Peking University

Scientometrics, 2015, vol. 105, issue 3, No 9, 1527-1552

Abstract: Abstract Scientific mapping has now become an important subject in the scientometrics field. Journal clustering can provide insights into both the internal relations among journals and the evolution trend of studies. In this paper, we apply the affinity propagation (AP) algorithm to do scientific journal clustering. The AP algorithm identifies clusters by detecting their representative points through message passing within the data points. Compared with other clustering algorithms, it can provide representatives for each cluster and does not need to pre-specify the number of clusters. Because the input of the AP algorithm is the similarity matrix among data points, it can be applied to various forms of data sets with different similarity metrics. In this paper, we extract the similarity matrices from the journal data sets in both cross citation view and text view and use the AP algorithm to cluster the journals. Through empirical analysis, we conclude that these two clustering results by the two single views are highly complementary. Therefore, we further combine text information with cross citation information by using the simple average scheme and apply the AP algorithm to conduct multi-view clustering. The multi-view clustering strategy aims at obtaining refined clusters by integrating information from multiple views. With text view and citation view integrated, experiments on the Web of Science journal data set verify that the AP algorithm obtains better clustering results as expected.

Keywords: Scientific mapping; Affinity propagation; Text mining; Link analysis; Multi-view clustering (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11192-015-1682-7

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