关键词: K-medoids聚类算法, Quick Access Recorder(QAR)数据, 标准欧氏距离, 近邻搜索, 距离最大法, 油耗分类

Abstract: To analyze the aircraft fuel consumption in given external environment,this paper proposed a neighborhood search K-medoids clustering algorithm (IK-medoids) based on the maximum distance method.According to the idea that the sample points with the farthest distance cannot be divided into the same cluster,the maximum distance method is used to select the initial center.And then,the center neighborhood is determined by the standardized Euclidean distance between the initial center and rest samples.What’s more,theregeneration of initial center is conducted by the proposed nearest neighbor searching strategy,efficiently reducing the iteration time.The contrast experiments were conducted on datasets with different size of the same aircraft model and flight segment,so as to classify the fuel flow data according to the gross weight,cruise altitude,flight distances and flight environment.The results demonstrate that the proposed IK-medoids algorithm outperforms common K-medoids algorithms,and provides a new angle for further analysis on the fuel consumption in flight process.

Key words: Fuel consumption classification, K-medoids clustering algorithm, Maximum distance method, Nearest neighbor searching strategy, QAR data, Standard euclidean distance