پایان نامه رایگان با موضوع Environment، Technology، خوشه بندی

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پیوست‌ها
کدهای مربوط به محاسبه‌ی شاخص دیویس بولدین در متلب:
clc
clear
close all
load data4%اسم دیتای ذخیره شده
X=untitled;%اسم جدول دیتای ما
for k=1:25;
[idx,C,sumd,D] = kmeans(X,k,’emptyaction’,’singleton’);
% for kk=1:k
%
% % plot3(X(idx==kk,1),X(idx==kk,2),X(idx==kk,3),’r.’,’MarkerSize’,kk)
% % hold on
%
% plot3(X(:,1),X(:,2),X(:,3),’k.’,’MarkerSize’,6)
% hold on
%
% % plot(C(:,4),C(:,5),’kx’,…
% % ‘MarkerSize’,12,’LineWidth’,2)
% plot3(C(:,1),C(:,2),C(:,3),’ro’,…
% ‘MarkerSize’,6,’LineWidth’,2)
%
% end
[t,r] = db_index(X, idx,C);
DB(k)=t;
k;
end
Kopt=find(DB==min(DB),1);
figure
plot(DB)
ylabel(‘DB index’)
xlabel(‘number of clusters’)
text(5,max(DB)-1,[‘Kopt = ‘,num2str(Kopt)],’FontSize’,12)
[idx,C,sumd,D] = kmeans(X,Kopt,’emptyaction’,’singleton’);
figure
for kk=1:Kopt
% plot3(X(idx==kk,1),X(idx==kk,2),X(idx==kk,3),’r.’,’MarkerSize’,kk)
% hold on
plot3(X(:,1),X(:,2),X(:,3),’k.’,’MarkerSize’,6)
hold on
% plot(C(:,4),C(:,5),’kx’,…
% ‘MarkerSize’,12,’LineWidth’,2)
plot3(C(:,1),C(:,2),C(:,3),’ro’,…
‘MarkerSize’,6,’LineWidth’,2)
xlabel(‘R_{normalized}’)
ylabel(‘F_{normalized}’)
zlabel(‘M_{normalized}’)
xlim([0 1])
ylim([0 1])
zlim([0 1])
grid
end
%figure
%for kk=1:Kopt
%plot(X(:,4),X(:,5),’k.’,’MarkerSize’,6)
%hold on
% plot(C(:,4),C(:,5),’kx’,…
% ‘MarkerSize’,12,’LineWidth’,2)
%plot(C(:,4),C(:,5),’ro’,…
% ‘MarkerSize’,6,’LineWidth’,2)
%xlabel(‘SC_{normalized}’)
%ylabel(‘S_{normalized}’)
%xlim([0 1])
%ylim([0 1])
%grid
%end
reply1=menu(‘Do you want to save? ‘,’ BALE’,’NOCH’);
if (reply1==1)
esm=input(‘Esme file save shode chi bashe? inja benevis’,’s’);
save(esm)
end
کدهای مربوط به خوشه بندی با الگوریتم k-means در مدل سوم با متلب:
clc
clear
load database
% data
x=data;
C0=initial; %% mokhtasaate maraakeze avallie
[idx,c]=kmeans(x,[],’start’,C0);
%%%%%%%%%%%%% baraye rasm
figure(1);
plot(x(:,1),x(:,2),’o’)
hold on
plot(c(:,1),c(:,2),’r*’)
%bad az ejraye barname idm hamon cluster hayie ke mikhay to datahaye samte
%rast miad to workspace man pain nemodaresh ro bar be sorate mile barat
%ezafe mikoone
figure(2);
bar(idx(1:347,1),’DisplayName’,’idx(1:347,1)’);figure(gcf)
Abstract
One of the main components of success in various companies is identification valuable customers which has received considerable attention in comparison with the past. Chain Stores are facing with several kinds of customer groups. Due to Limited resources, they must rate customers according to their values and allocate appropriate marketing resources to valuable customers to turn huge profits. So data minig techniques will be used for segmenting customers, A great number of researches have been conducted in this subject, Most of these studies have used RFM model to Segmentation of customers. This model is composed of three indexes Recency, Frequency, and Monetary value to analyses consumers’ purchasing behavior and indicate customer behavior value. In the present research, Segmentation of customers base on value will be represented by Comprehensive methodology include three model Segmentations using developed RFM, SOM, and K-Means. In order to identify customer, Transaction and demographic data were analyzed. The proposed models were implemented in Iran Apple Center chain stores and 347 customers were analyzed. For transactional data, the recorded transactions in the store data center were used. For Demographic data each customer were asked telephonic. This data were segmented with three different models and finally the models have been evaluated and compared with the Davies Bouldin index and Sum of the Squared Error. According to Davies Bouldin index in this case study the efficiency of first model is the best, but according to SSE the second model is the best and it is because of the different nature of these two criteria.
Keywords:
Chain Stores, Customers Segmentation, Customers’ Value, Data Mining, RFM, SOM, K-Means
Alzahra University
Faculty of Engineering
M.Sc.Thesis of Industrial engineering
Presentation and Comparison of Three Tow-step Models for Customers’ Segmentation Based on Value Using K-Means, SOM and RFM Data Mining Tools
Case Study: Apple Center of Iran
Advisor
Dr. Reza Samizadeh
Prof. Seyed Mohammad Seyed Hoseini
Reader
Dr. Mohammad Jafar Tarokh
By
Fatemeh Adeli Koudehi
901514013
October. 2013
1 Value
2 Chain stor
3 Data Minig
4 Recency, Frequency, Monitory
5

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