Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/23515
標題: 應用資料探勘探討線上訂票乘客行為-以台鐵西幹線為例
Applying Data Mining Techniques to Explore Passengers'' Behavior on Online-booking- A Case of Taiwan Railway Western Mainline
作者: 陳民祐
Chen, Min-Yu
關鍵字: 資料探勘
data mining
線上訂票
集群
分類
自我映射神經網絡
online-booking
clustering
classification
kohonen self-organized mapping
出版社: 行銷學系所
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摘要: 在網際網路的日益普及下,台鐵線上訂票系統已經成為民眾使用台鐵服務的重要管道,也因此與旅客之間發展出新的互動關係。藉由台鐵龐大的線上訂票資料庫,可供台鐵分析以進行顧客關係管理。面對競爭日趨激烈的台灣大眾運輸市場,台鐵如何善用數量豐富的交易資料庫,成為一個重要的課題。 本研究旨在應用資料探勘中的集群化、分類來對台鐵線上訂票旅客進行探討。首先比較三種集群方法:K-means、RFM搭配K-means、Kohonen Self-Organized Mapping搭配K-means、再利用三種統計指標比較集群效果;之後選擇最佳的集群方法運行決策樹以建構顧客分類預測模式。並會使用主成分分析以較少指標的方式鎖定市場區隔,制定行銷方案,俾作為鐵路局以後改善營運策略之參考。 研究結果發現,Kohonen Self-Organized Mapping搭配K-means的集群方法相當適合大型的線上交易資料庫。且兩階段集群法在統計指標方面的表現,都優於一階段集群法。
The use of internet is popular in recent years and the Taiwan Railways online-booking system has been one of the important ways to order ticket so that Taiwan Railway Administration could develop new interactive relationship with passengers. By the large records of Taiwan Railways online-booking system, Taiwan Railway Administration has the opportunity to adopt customer relationship management. Facing the fierce Taiwan public transport market, the way of how to use abundant transaction records is a very important issue today. This study applied clustering analysis and classification to explore passengers’ behavior. First we will use three different clustering methods: K-means only, RFM with K-means and Kohonen Self-Organized Mapping with K-means. After comparing the clustering index, the best result will make a classification and quickly distinguish customer group belongings. In the mean time we apply principal component analysis to extract fewer indexes to make Taiwan Railway Administration choose the focus group and plan the marketing strategy. The result found that the way of Kohonen Self-Organized Mapping with K-means is suitable with large records of Taiwan Railways online-booking system. The two-step cluster algorithm is also better than one-step cluster algorithm.
URI: http://hdl.handle.net/11455/23515
其他識別: U0005-1107201222354000
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1107201222354000
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