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나노 인포매틱스 기반 구축을 위한 구글 트렌드와 데이터 마이닝 기법을 활용한 나노 기술 트렌드 분석 KCI 등재

Nano Technology Trend Analysis Using Google Trend and Data Mining Method for Nano-Informatics

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  • URLhttps://db.koreascholar.com/Article/Detail/338608
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한국산업경영시스템학회지 (Journal of Society of Korea Industrial and Systems Engineering)
한국산업경영시스템학회 (Society of Korea Industrial and Systems Engineering)
초록

Our research is aimed at predicting recent trend and leading technology for the future and providing optimal Nano technology trend information by analyzing Nano technology trend. Under recent global market situation, Users’ needs and the technology to meet these needs are changing in real time. At this point, Nano technology also needs measures to reduce cost and enhance efficiency in order not to fall behind the times. Therefore, research like trend analysis which uses search data to satisfy both aspects is required. This research consists of four steps. We collect data and select keywords in step 1, detect trends based on frequency and create visualization in step 2, and perform analysis using data mining in step 3. This research can be used to look for changes of trend from three perspectives. This research conducted analysis on changes of trend in terms of major classification, Nano technology of 30’s, and key words which consist of relevant Nano technology. Second, it is possible to provide real-time information. Trend analysis using search data can provide information depending on the continuously changing market situation due to the real-time information which search data includes. Third, through comparative analysis it is possible to establish a useful corporate policy and strategy by apprehending the trend of the United States which has relatively advanced Nano technology. Therefore, trend analysis using search data like this research can suggest proper direction of policy which respond to market change in a real time, can be used as reference material, and can help reduce cost.

목차
1. 서 론
 2. 이론적 배경 및 방법
  2.1 기존의 연구
  2.2 연구 방법
 3. 분석 결과
  3.1 빈도 수 기반 트렌드 검출 및 시각화
  3.2 키워드 간의 상관관계 분석
  3.3 네트워크 분석
  3.4 군집 분석
 4. 결 론
 References
저자
  • 신민수(한양대학교 경영대학) | Minsoo Shin (School of Business, Hanyang University)
  • 박민규(한양대학교 경영대학) | Min-Gyu Park (School of Business, Hanyang University)
  • 배성훈(한국과학기술정보연구원) | Seong-Hun Bae (Korea Institute of Science and Technology Information) Corresponding author