개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2011.5
- 수록면
- 2,010 - 2,014 (5page)
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초록· 키워드
오류제보하기
The utilization ratio of wind energy, which is one of the renewable energy sources, is increased around 25% since last 15 years. However, the parameters such as performance of wind turbines and climate features are not analyzed adequately. At the analysis stage of these parameters, data mining techniques are required to be used. In this study, the agglomerative hierarchical clustering method which is one of the data mining techniques is used to analyze the provinces located in the Central Anatolia Region of Turkey in terms of average wind speed. Nearest neighbor algorithm is used as the clustering algorithm. Euclidean, Manhattan and Minkowski distance metrics are used determine the optimum hierarchical clustering results in this algorithm. The achieved clustering results based on Euclidean distance metric provide the optimum inferences to expert according to other distance metrics.
목차
Abstract
Ⅰ. INTRODUCTION
Ⅱ. THE AGGLOMERATIVE HIERARCHICAL CLUSTERING METHOD USED
Ⅲ. UML ACTIVITY DIAGRAM OF THE TOOL DESIGNED
Ⅳ. INTERFACE LAYER OF THE TOOL DESIGNED
Ⅴ. EVALUATION OF CLUSTERING RESULTS
Ⅵ. CONCLUSIONS
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2013-560-000301586