A study on the stratification of long-tail customers in civil aviation based on a cluster ensemble

聚类分析 轮廓 计算机科学 堆积 数据挖掘 民用航空 兰德指数 贝叶斯概率 集合预报 混合模型 算法 人工智能 航空 工程类 物理 核磁共振 航空航天工程
作者
Yi Zong,Ying Li,Enze Pan,Simin Chen,Jingkuan Zhang,Binbin Gao
出处
期刊:Journal of Intelligent and Fuzzy Systems [IOS Press]
卷期号:46 (3): 5783-5799
标识
DOI:10.3233/jifs-234155
摘要

Stratifying long-tail customers and identifying high-quality customers with high growth potential are crucial for civil aviation companies to explore new profit growth points. This paper proposes a long-tail customer stratification model based on clustering ensemble to address the problems of insufficient attention to long-tail customers in previous studies and the low accuracy and lack of accuracy testing of single clustering algorithms. First, the Bayesian information criterion is used to determine the optimal number of clusters. Then, an ensemble framework integrating the Gaussian mixture model, spectral clustering, Two step clustering and K-means algorithm is constructed, and the stacking and bagging ensemble methods are used for the cluster ensemble. Finally, three different indicators are used to evaluate the algorithm performance. Experimental results indicate that compared with single clustering algorithms, the Stacking algorithm increases the silhouette coefficient by 14.77% to 27.11%, the Calinski-Harabasz index by 38.83% to 122.18%, and the Davies-Bouldin Index by 19.38% to 98.04%. This indicates that each clustering has high cohesion and separation, with samples within a category being more closely related and those between categories having clear boundaries. It shows that the Stacking algorithm more accurately stratifies long-tail customers with similar consumption behaviors into different categories, achieving customer stratification.
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