입원기간 유사성 분석을 위한 Hierarchical Clustering Method 실험

• 영문명: Hierarchical Clustering Method experiments to analyze the similarity of hospitalization
• 발행기관: 한국IT마케팅학회
• 저자명: 김승연(Seung Yeon Kim) 정용규(Yong Gue Jung)
• 간행물 정보: 『한국IT마케팅학회 논문집』한국IT마케팅학회 논문집 1권 1호, 20~21쪽, 전체 2쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2014.11.27

영문 초록

By the openness of recent public data, big data has been attracting attention as a new industry. Big data is variously used in various fields. And is effectively applied through the study and analysis of existing data. Data mining, and can extract information on the basis of big data, are efficiently used to move large amounts of data. Data mining may be applied in various industrial fields, particularly the medical part, by using the patient's data, and the purpose of health care, have been used as research materials in the health industry In this paper, for efficient analysis of the medical data, and tries to analyze the experimental hospitalization period depending on the age of the region through the discharge data of the patient. Here there is a purpose to try to investigate the effective data mining techniques. Thus, in order to implement Hierarchical Clustering Method clogging hierarchical community analysis experiments Gunjipufa technique is one method for data mining, using the method of converting a text to improve the performance of the experiment attribute vector as a special technology Experimental I want to compare the crowding distance. In this paper, we evaluate the performance based on the result of the execution by selecting a special technique for improving the performance of the experiments and the experimental method described above in Gunjipufa technique.

목차

Abstract

Ⅰ. 서론

Ⅱ. 관련연구

Ⅲ. 모의실험

Ⅳ. 결론

참고문헌

키워드

빅데이터 군집화 계층적 군집 분석 텍스트를 속성벡터로 변환방법