1 出處： fuzzy systems, 2000. fuzzy ieee 2000. the ninth ieee international conference volume: 2,...
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出處 ： Fuzzy Systems, 2000. FUZZY IEEE 2000. The Ninth IEEE International Conference Volume: 2 , 2000 Page(s): 621 -626
作者 ： Tsuen-Ho Hsu; (I-SHOU University) Kao-Ming Chu; (Soochow University) Hei-Chun Chan; (Wen Tzao College)
資管所在職專班二年級指導老師：陳榮昌 老師報告學生：林合成 (9154616) Email ： [email protected]
The Fuzzy Clustering on Market SegmentThe Fuzzy Clustering on Market Segment
模 糊 理 論
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1. Introduction
2. Traditional clusteringclustering
3. The fuzzy clustering analysis methodThe fuzzy clustering analysis method
4. Case studyCase study
5. Conclusion (Comment)
『 Outline 』
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1.『 Introduction 』
Market Segmentation
Tool ： clustering analysis
Traditional method ： crisp partition but cannot fit the real product market.
In fact different segments overlap each other.
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市場區隔的概念，係由 Wendell R. Smith於 1956年首先提出，其定義為將市場上某方面需求相似的顧客或群體歸類在一起，建立許多小市場，使這些小市場之間存在某些顯著不同的傾向，以便使行銷人員能更有效地滿足不同市場（顧客）不同的慾望或需要，因而強化行銷組合的市場適應力。
Pride & Farell（ 1988）指出，市場區隔的定義乃是以市場需求面的發展為基礎，將市場上的顧客分為幾個需求類似的群體，每一群體或區隔（ Segment）可採用一種行銷組合（ Marketing mix）來滿足。
『市場區隔之定義』
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2.『 Traditional clustering analysis methodclustering analysis method 』
Clustering.
Method for categorizing objects into groups there
are homogeneous along a range of characteristic.
Its effectiveness depends on the use of :
Variable.
Method.
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Type of cluster analysis
1.nonoverlapping
Hierarchical.
Nonhierarchical.
2.nonoverlapping
Fuzzy.
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3.『 The fuzzy clustering analysis methodThe fuzzy clustering analysis method 』
One sample can belong to two or more groups.
Fuzzy clustering ：hard clustering & soft clustering
Fuzzy clustering method ：fuzzy relation to perform fuzzy clustering.
based on objective function to determine fuzzy clustering
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Fuzzy C-Means (FCM)
Bezdek’s(1981) FCM remains the most commonly used.
Use Xie and Beni(1991) to revise Bezdek’s FCM.
To determine the proper number of segments and the segmentation validity.
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Bezdek’s FCM objective function
Xk : each sample Vi : each group center
uik is membership grade that Xk belong to Vi
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Fuzzy membership matrix M
c
j
q
jk
ik
ik
dd
m
1
1/2
1
ikikd cu
Distance from point k to current cluster centre i
Distance from point k to other cluster centres j
Point k’s membership of cluster i
Fuzziness exponent
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Compactness and Separation Validity function :
its purpose is to solve the cluster numbering ,C.
The smaller the value of S(c), the better the compactness and separation between the clustering groups of in-cluster samples.
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Fuzzy c-partition
Kc
iallforUCØ
jiallforØCC
UC
i
ji
c
ii
2
1
All clusters C together fill the whole universe U.
Remark: The sum of memberships for a data point
is 1, and the total for all points is K
Not valid: Clusters do overlap
A cluster C is never empty and it is
smaller than the whole universe U
There must be at least 2 clusters in a c-partition and
at most as many as the number of data points K
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4. 『 Case studyCase study 』
case : buyers of three consumer housing selling sites located in Kaohsiung city from April 1 to May 31, 1999.(total 350 questionnaires)
335 questionnaires returned, 312 are usable
effective response rate of 89%
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Purchase housing decision criterion
Using questionnaires find 36 benefit variable and
from 36 reduce to 7.
1.life style and quality.
2.payment and loan.
3.planning and design.
4.company credibility and house service quality.
5.safty.
6.building’s and re-selling.
7.information equipment.
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Fuzzy clustering analysis
if set m=2,c=2~10 convergence value ε=0.001
when c=5 the compactness and separation validity value (s=0.2800) is the minimum value.
the number of the optimal segment is 5
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Findings
1.different between integer value (hard segment) and real value (soft segment).
it often happen higher and lower_account condition, when we want to forecast the size of market. But it shows opposite condition in segment 3.
2.A gap between integer value and real value.
it shows consumer market is a dynamic and uncertainly condition.
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Finding :
1.Higher α-cuts , higher customers’ loyalty.
2.The membership grade of more than half of each group is 0.5. segment 2 and segment 3 are unstable.
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5. 『 Conclusion (Comment) 』
在真實的世界，很多問題的 boundaries 並不是很明確，如果用 crisp partition 可能會錯過一些 information ．此篇 paper的貢獻在於利用 fuzzy cluster method 來作 market segment，提供各種不同的 market situations 來幫助決策者作決策。
與其他 clustering 方法的比較： k-mean 、 SOM 、 ART 等可再深入探討。
Fuzzy 的延伸應用：在推薦機制上，雖有推薦產品的產生，如果能將輸出結果作排序，是否可減少 consumer 的負擔。