■■■■■■■ Fig. 2 Connections between Author Research Fields in the Top 10 Big Data Countries (2018)Note. Distance: hclust (*, “ward.D2”). We used the bibliographic data of Web of Science Core collection.Fig. 3 Similarity Dendrogram by Country: Links between Author Research Areas in the Top 10 Big Data Countries─ 13 ─■■■■■■■■■ff■■■■■■■■■■■■■ff■■■■■■■■■■■■■■■■■■■ff■■■■■ff■■■■■■■■■■■■■■■■■■ff■■■ ■■■■■■■■■■■■■■■■■■■■ff■■■■■ff■■■■■■■■■■■■■■■■■■ff ■■■■ff■■■■■■■■■■■■■■■■■■ff■■■■■■■■■ Note: This indicates connections with a strength of 1% or more. We used the bibliographic data of Web of science Core collection.biology and biochemistry (2) and is considered to have many and wide connections with no exceptionally strong ones.The interdisciplinary links of the top 10 countries were analyzed using hierarchical cluster, using countries as individuals and interdisciplinary links as variables and visualized using a dendrogram (Fig. 3).In Fig. 3, the top 10 big data countries were classified into three groups for the ease of interpretation: Group 1 comprised India, China, and South Korea. Group 2 included Spain, Australia, and Italy. Group 3 consisted of Canada, Germany, the United States, and England.This study contributes to the Industrial development by identifying cross-disciplinary fusion patterns in big data based on Innovation theory.Innovation is a thinking approach that creates new knowledge (value) from “a new combination of existing knowledge and existing knowledge,” which Schumpeter called new combination in business administration. This study uses the definition of existing knowledge as an interdisciplinary field and considers new knowledge (value) created by fusion 5. Discussion and Conclusions
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