Analysis Method for Customer Value of Aviation Big Data Based on LRFMC Model

Tao, Yang

doi:10.1007/978-981-15-7981-3_7

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Therefore, a novel customer value identification model is obtained by adding the customer's accumulated flight miles M in the time period and the discount factor C corresponding to the class of travel to replace the customer's spending amount in the original model on the basis of the RFM model. Moreover, the length of membership of airline members can also influence the customer value to a certain extent, so this paper incorporates the length of customer relationship L into the model and obtains the LRFMC model [37]. In summary, this case uses the duration of membership L, consumption interval R, consumption frequency F, flight mileage M and average discount factor C as airline identification customer value indicators, and the specific meaning of each indicator is shown in Table 5 below.…”

Section: Methodsmentioning

confidence: 99%

A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer Algorithm

et al. 2022

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Over the years, research on fuzzy clustering algorithms has attracted the attention of many researchers, and they have been applied to various areas, such as image segmentation and data clustering. Various fuzzy clustering algorithms have been put forward based on the initial Fuzzy C-Means clustering (FCM) with Euclidean distance. However, the existing fuzzy clustering approaches ignore two problems. Firstly, clustering algorithms based on Euclidean distance have a high error rate, and are more sensitive to noise and outliers. Secondly, the parameters of the fuzzy clustering algorithms are hard to determine. In practice, they are often determined by the user’s experience, which results in poor performance of the clustering algorithm. Therefore, considering the above deficiencies, this paper proposes a novel fuzzy clustering algorithm by combining the Gaussian kernel function and Grey Wolf Optimizer (GWO), called Kernel-based Picture Fuzzy C-Means clustering with Grey Wolf Optimizer (KPFCM-GWO). In KPFCM-GWO, the Gaussian kernel function is used as a symmetrical measure of distance between data points and cluster centers, and the GWO is utilized to determine the parameter values of PFCM. To verify the validity of KPFCM-GWO, a comparative study was conducted. The experimental results indicate that KPFCM-GWO outperforms other clustering methods, and the improvement of KPFCM-GWO is mainly attributed to the combination of the Gaussian kernel function and the parameter optimization capability of the GWO. What is more, the paper applies KPFCM-GWO to analyzes the value of an airline’s customers, and five levels of customer categories are defined.

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Section: Methodsmentioning

confidence: 99%

A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer Algorithm

et al. 2022

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“…Combining the improved model with classical machine learning classification algorithms such as SVM, KNN, GBDT and NN, etc., it is widely used in passenger behavior prediction (S. Q. Pang & Liu, 2011), personalized recommendation (Tao, 2020), flight delay prediction (Jiang, Liu, Liu, & Song, 2020) and other aviation fields in business. However, experimental studies on identifying passengers' willingness to choose a seat for a fee are still limited.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Air Passengers' Willingness to Pay for Seat Selection for Imbalanced Data Based on Improved XGBoost

Hong

Tang

et al. 2022

International Journal of Cognitive Informatics and Natural Intelligence

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Passenger-paid seat selection is one of the important sources of ancillary revenue for airlines, and machine learning-based willingness-to-pay identification is of great practicality for airlines to accurately tap potential willing passengers. However, affected by periodic statistical errors, air passenger order data often has some problems such as high noise, high latitude, and unbalanced category. In view of this, this paper proposes a method for identifying air passengers' willingness to pay for seat selection based on improved XGBoost, which is improved and integrated from three stages: data, feature, and algorithm. The feasibility of the proposed multi-stage improved integration method is verified by real airline passenger dataset, and the experimental results show that the proposed improved method has better classification effect when compared with the classical six imbalance classification models, which provides a basis for accurate marketing of airline paid seat selection programs.

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How is the Power of the Baidu Index for Forecasting Hotel Guest Arrivals? –A Case Study of Guilin

Liu

et al. 2022

Communications in Computer and Information Science

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Analysis Method for Customer Value of Aviation Big Data Based on LRFMC Model

Cited by 5 publications

References 4 publications

A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer Algorithm

A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer Algorithm

Recognition of Air Passengers' Willingness to Pay for Seat Selection for Imbalanced Data Based on Improved XGBoost

How is the Power of the Baidu Index for Forecasting Hotel Guest Arrivals? –A Case Study of Guilin

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