• 전자공학회논문지CI
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• 제41권6호
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• pp.39-46
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• 2004

클러스터 분석에 있어 중요한 문제 중의 하나는 주어진 데이터에 내재된 적절한 클러스터의 수를 찾아내는 것이다. 본 논문에서는 이러한 클러스터의 개수를 체계적으로 결정하기 위하여 IRC (Input Representation Coverage) 개념을 새로이 정의하고, 이를 이용하여 주어진 데이터에 적합한 클러스터의 개수를 자동 결정하는 방법을 제시한다. 또한, 이러한 방법의 유용성 및 응용성을 알아보기 위하여 가상 데이터를 가지고 분석 실험을 하였으며, 실험을 통해 데이터에 내재된 실제 클러스터의 개수를 찾아내는 데에 제안된 방법이 매우 유용하게 사용될 수 있음을 보여준다.

• 동의생리병리학회지
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• 제25권5호
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• pp.765-772
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• 2011

Knowledge which is represented by formal logic are widely used in many domains such like artificial intelligence, information retrieval, e-commerce and so on. And for medical field, medical documentary records retrieval, information systems in hospitals, medical data sharing, remote treatment and expert systems need knowledge representation technology. To retrieve information intellectually and provide advanced information services, systematically controlled mechanism is needed to represent and share knowledge. Importantly, medical expert's knowledge should be represented in a form that is understandable to computers and also to humans to be applied to the medical information system supporting decision making. And it should have a suitable and efficient structure for its own purposes including reasoning, extendability of knowledge, management of data, accuracy of expressions, diversity, and so on. we call it ontology which can be processed with machines. We can use the ontology to represent traditional medicine knowledge in structured and systematic way with visualization, then also it can also be used education materials. Hence, the authors developed an Shanghanlun ontology by way of showing an example, so that we suggested a methodology for ontology development and also a model to structure the traditional medical knowledge. And this result can be used for student to learn Shanghanlun by graphical representation of it's knowledge. We analyzed the text of Shanghanlun to construct relational database including it's original text, symptoms and herb formulars. And then we classified the terms following some criterion, confirmed the structure of the ontology to describe semantic relations between the terms, especially we developed the ontology considering visual representation. The ontology developed in this study provides database showing fomulas, herbs, symptoms, the name of diseases and the text written in Shanghanlun. It's easy to retrieve contents by their semantic relations so that it is convenient to search knowledge of Shanghanlun and to learn it. It can display the related concepts by searching terms and provides expanded information with a simple click. It has some limitations such as standardization problems, short coverage of pattern(證), and error in chinese characters input. But we believe this research can be used for basic foundation to make traditional medicine more structural and systematic, to develop application softwares, and also to applied it in Shanghanlun educations.