Mining Multi-Dimension Rules in Multiple Database Segmentation-on Examples of Cross Selling

Abstract

在今日以客戶為導向的市場中，“給較好的客戶較好的服務”的概念已經逐漸轉變為“給每一位客戶適當的服務”。藉由跨域行銷(cross-selling)的方式，企業可以為不同的客戶提供適當的服務及商品組合。臺灣的金融業近年來在金融整合中陸續成立了多家金融控股公司，希望藉由銀行、保險與證券等領域統籌資源與資本集中，以整合旗下子公司達成跨領域的共同行銷。這種新的行銷方式需要具有表達資料項目間關係的資訊技術，而關聯規則(association rule)是一種支援共同行銷所需之資料倉儲中的極重要元件。\n傳統關聯規則的挖掘可以用來找出交易資料庫中客戶潛在的消費傾向。如果得以進一步的鎖定是那些客戶在什麼時間、什麼地點具有這種消費傾向，我們可藉此制定更精確、更具獲利能力的行銷策略。然而，大部分的相關習成技術都假設挖掘出的規則在資料庫的每一個區間都是一樣有效的，然而這顯然不符合大多數的現實狀況。\n本研究主要著眼於如何有效率的在不同維度、不同大小的資料庫區域中挖掘關聯規則。藉此發展出可以自動在資料庫中產生分割的機制。就此，本研究提出一個方法找出在各個分割中成立的關聯規則，此一方法具有以下幾個優點：\n1. 對於找出的關聯規則，可以進一步界定此規則在資料庫的那些區域成立。\n2. 對於使用者知識以及資料庫重覆掃瞄次數的要求低於先前的方法。\n3. 藉由保留中間結果，此一方法可以做到增量模式的規則挖掘。\n本研究舉了兩個例子來驗證所提出的方法，結果顯示本方法具有效率及可規模化方面均較以往之方法為優。

In today’s customer-oriented market, vision of “For better customer, the better service” becomes “For every customer, the appropriate service”. Companies can develop composite products to satisfy customer needs by cross-selling. In Taiwan’s financial sector, many financial holding companies have been consecutively founded recently. By pooling the resources and capital for banking, insurance, and securities, these financial holding companies would like to integration information resources from subsidiary companies for cross-selling. This new promotion method needs the information technology which can present the relationship between items, and association rule is an important element in data warehouse which supports cross-selling.\nTraditional association rule can discover some customer purchase trend in a transaction database. The further exploration into targets as when, where and what kind of customers have this purchase trend that we chase, the more precise information that we can retrieve to make accurate and profitable strategies. Moreover, most related works assume that the rules are effective in database thoroughly, which obviously does not work in the majority of cases.\nThe aim of this paper is to discover correspondent rules from different zones in database. We develop a mechanism to produce segmentations with different granularities related to each dimension, and propose an algorithm to discover association rules in all the segmentations. The advantages of our method are:\n1. The rules which only hold in several segmentations of database will be picked up by our algorithm.\n2. Mining all association rules in all predefined segmentations with less user prior knowledge and redundant database scans than previous methods.\n3. By keeping the intermediate results of the algorithm, we can implement an incremental mining.\nWe give two examples to evaluate our method, and the results show that our method is efficient and effective.