Emergent Rough Set Data Analysis

2004 ◽  
pp. 343-361 ◽  
Author(s):  
Yasser Hassan ◽  
Eiichiro Tazaki
Keyword(s):  
Data Analysis ◽  
Rough Set

Research to E-Commerce Customers Losing Predict Based on Rough Set

2011 ◽  
Vol 58-60 ◽  
pp. 164-170 ◽  
Author(s):  
Ming Jun Wang ◽  
Shu Xian Deng
Keyword(s):  
Data Analysis ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Customer Behavior ◽  
Purchase Behavior ◽  
Decision Table ◽  
Purchasing Behavior ◽  
Expert Opinions ◽  
Loss Prediction

The present paper based on rough set theory is to analyze the reason of an e-commerce customers losing. The e-commerce is virtual, customers purchase behavior is random, and there is the 20/80 theory. The focus to the e-commerce customers losing predict is to bring enterprise 80percent profits or frequent buying clients, they will be the study samples. Therefore, we must first find out these clients from numerous customers, analyze their purchasing behavior, and it is one of the important links loss prediction. This process may be realized by customer behavior data clustering. We have analyzed the data in one e-commerce database, and according to a certain algorithm has classified these customers, one kind is superior customers, one kind is general customers, the rest is temporary customers. And a lot of questionnaire survey have been done to these kinds of customers, and then combining e-commerce expert opinions formed the customers data analysis and decision table, then the algorithm, which is the decision table blindly delete attribute reduction algorithm, is adopted to process the attributes reduction to the decision table. Then, we get the reduction table of the customers’ data analysis and decision. According to the reduction table, we summarize e-commerce customers’ loss decision rule. Through these decision-making rules, we can predict these losing customers, and take timely measures necessary to retain.

scholarly journals EMBEDDEDNESS OF ENTREPRENEURS IN RURAL AREAS: A COMPARATIVE ROUGH SET DATA ANALYSIS

2010 ◽  
Vol 101 (5) ◽  
pp. 538-553 ◽  
Author(s):  
ALIYE AHU AKGÜN ◽  
PETER NIJKAMP ◽  
TÜZIN BAYCAN ◽  
MARTIJN BRONS
Keyword(s):  
Data Analysis ◽  
Rough Set ◽  
Rural Areas

Rough Set Analysis and Formal Concept Analysis

2011 ◽  
pp. 108-127
Author(s):  
Yiyu Yao
Keyword(s):  
Data Analysis ◽  
Comparative Study ◽  
Rough Set ◽  
Formal Concept Analysis ◽  
Concept Analysis ◽  
Formal Concept ◽  
Intelligent Data Analysis ◽  
Unified Framework

Rough set analysis (RSA) and formal concept analysis (FCA) are two theories of intelligent data analysis. They can be compared, combined and applied to each other. In this chapter, we review the existing studies on the comparisons and combinations of rough set analysis and formal concept analysis and report some new results. A comparative study of two theories in a unified framework provides a better understanding of data analysis.

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