一种函数型数据系统聚类分析方法应用

基金项目: 国家自然科学基金资助项目(70771004,70521001,70531010); 北京市自然科学基金资助项目(9052006)

详细信息

A Hierarchy Cluster Method for Functional Data

1.
School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
2. Department of Neural-internal-medicine, Beijing Tiantan Hospital, Beijing 100050, China
3. Canada International School, Beijing 100027, China

摘要: 提出了一种函数型数据的距离定义,分析了该距离与欧氏距离的关系,并给出一种函数型数据的系统聚类分析方法和具体算法,以及在低维空间对原始数据进行直观表达的方法。采用实证研究表明,该方法可以直观、有效地实现函数型数据的系统聚类分析。
- 主成分分析 /
- 聚类分析 /
- 成分
Abstract: An analysis is made of the mechanism of Functional Principal Component Analysis, and a new method of hierarchy clustering for functional data is presented, with which visualizing the original high dimensional datum in a low dimensional space is realizable. A definition of distance between functions is given. The relationship between the argued form of distance and the Euclidean distance is investigated. To prove validity, the proposed method is applied to classify the quality of cookies from curves representing the resistance of dough observed during the kneading process.
- principal component analysis /
- cluster analysis /
- components

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