英文摘要 |
In recent years, more and more airlines regard on-line booking as the basicfunction of their homepage. Since the Internet becomes more popular day by day,the traditional travel agencies are facing the crisis of having the middleman removed.However, a new interaction has appeared between airlines and passengers.While the e-commerce develops vigorously, the airlines go through passengers'online booking behavior, namely to obtain more customer informationand transaction records. Therefore, how to use this data to understand the customers,experience suitable data processing technology, and provide the customizedmarketing service to riders, all have become issues of future airlines.This research will explore data mining to discuss airline passengers’ onlinebooking behaviors. First, we adopt the RFM model (Recency, Frequency, Monetary),the average mileage and classes as five customer value index items toprocess clustering for riders. The result will make a classification and quicklydistinguish customer group belongings. Again, we aim at service products classificationto understand the consumers’ behavior of each route. Finally, the associationanalysis is carried out for different trip purposes, thence appears customers’implicit connecting demands between all routes. |