網路交易型樣探勘技術之研究

The Study of Web Transaction Pattern Mining

關聯規則 (Association Rule) 探勘主要是從顧客的交易資料庫中發掘顧客所購買 之商品間的關聯性。這樣的資訊可以提供電子商務網站的管理者了解顧客的購物行為，以便設計適當的行銷活動（如交叉銷售）。而網路瀏覽型樣 (Web Traversal Pattern) 探勘主要則是從顧客網路活動的記錄中發掘大多數顧客在網站上的瀏覽路徑。這樣的資訊可以提供電子商務網站的管理者了解顧客的瀏覽行為，以便提供顧客瀏覽路線的建議或改善網站的設計。然而，在電子商務網站的競爭逐漸白熱之際，現今網站的管理者不能單獨考量這兩種資訊，必須同時考慮顧客在網站上的購物及瀏覽行為。有鑑於此，本論文提出一個有效率的網路交易型樣 (Web Transaction Pattern) 探勘的方法 來發掘顧客在瀏覽網站及購買商品之間的關聯性，並克服傳統網路交易型樣探勘方法在執行效能及記憶體使用上的缺點，以充分掌握顧客的行為，增加網站之收益。實驗的結果顯示，本論文所提出的方法能有效的發掘網路交易型樣。

Association rule mining discovers correlations among products from transactional data- bases. It can provide useful information to website operators for designing appropriate marketing activities, e.g., cross-selling. Web traversal pattern mining discovers access patterns among customers from web logs. It can provide useful information to customers for suggesting appropriate navigation paths and website operators for improving website structures. However, website operators consider not only the pure navigation behaviors, but also the purchasing behaviors of customers. In this point of view, this paper proposes a new algorithm for mining web transaction patterns. It can discover the associations among traveling and purchasing behaviors of customers and overcome the disadvantages of traditional methods. By this way, the enterprises can fully grasp the behaviors of customers and increase the income of website. The experimental results show that our proposed method can efficiently discover web transaction patterns.