臺灣博碩士論文加值系統

現今的市場是以顧客導向為主，在這競爭激烈的環境下，企業需要為顧客提
供適合的服務以提升競爭力，因此，企業擁有更精確的顧客資訊就可以制定較準
確且有利的策略。關聯規則是從交易資料庫中挖掘大部分顧客的購買行為之不同
產品之間的關連性，更進一步的我們可以挖掘出顧客購買行為模式和顧客特徵之
間的關連性。
本研究提出一個適合市場區隔應用的多維度關聯規則探勘技術，利用條件式
資料庫(conditional database) 挖掘出包含顧客特徵與顧客購買行為的多維度關聯
規則，不需要多次掃瞄目標資料庫並且結合集群的方法將數值屬性自動離散化。
本研究提供兩種不同角度的結果，一個是在不同的客戶特徵下去挖掘出經常被購
買的產品組合，另一個是在不同的產品組合之下，找出經常購買該產品的顧客特
徵，兩種角度的結果可以提供決策者做市場區隔和制訂更為精確的策略。

Today is a customer-oriented market. Enterprises need to give every customer
appropriate service. The more precise information can make accurate and profitable
strategies. Association rules provide correlations between data items in large numbers
of data. The further exploration is to discover relationship between customer’s
features and customer purchasing behaviors.
This paper proposes a new method to discover mining multidimensional
association rule for market segmentation. We use conditional databases to discover
multidimensional association rule, do not scan the target database many times and
combine cluster method to automatically discretize numerical-type attributes. Our
method analyzes CRM data from two different points of view. One is the product
combinations according to different customer features; another is the customer
features according to purchased products of customers. These two different points of
view can provide decision-makers to establish customer profiles, segment market and
make strategies more accurately.

摘 要...............................................................i
ABSTRACT...........................................................ii
誌 謝.............................................................iii
Table of Contents..................................................iv
List of Tables.....................................................vi
List of Figures...................................................vii
Chapter 1 Introduction..............................................1
1.1 Background......................................................................................................1
1.2 Motivation........................................................................................................4
1.3 Objective ..........................................................................................................5
Chapter 2 Related Work..............................................7
2.1 Single-dimensional association rule ................................................................7
2.2 Multidimensional association rule ...................................................................8
Chapter 3 Definitions..............................................15
3.1 Association Rule ............................................................................................15
3.2 Multidimensional Data and Itemsets .............................................................16
Definition. ....................................................................................................17
Chapter 4 Mining Multidimensional Association Rules................18
Part 1: Focus on Categorical-type Attributes.......................................................18
Step 1 : Find length-1 frequent items...........................................................19
Step 2 : Order and condense data.................................................................20
Step 3 : Partition database by length-1 frequent items ................................20
Step 4 : Find length-k frequent itemsets by conditional databases..............21
Part 2 : Focus on Numerical-type Attributes .......................................................23
Step 1 : Project values of numerical-type attributes on space. ....................24
Step 2 : Segment space. ...............................................................................25
Step 3 : Find all clusters to generate itemsets. .............................................25
Step 4 : Use minimum support threshold to prune clusters. ........................26
View 1: Discover popular products combinations according to customer features.
..............................................................................................................................28
View 2: Discover customer features according to purchased products ...............34
Chapter 5 Experiments..............................................40
5.1 Experiments for categorical-type dataset.......................................................40
5.2 Experiments for different points of views .....................................................52
5.2.1 Experiments for View 1......................................................................53
v
5.2.2 Experiments for View 2......................................................................56
Chapter 6 Conclusion and Future Work...............................59
References.........................................................61

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