Association Rule Retrieved from Web Log Based on Rough Set Theory

2007 ◽  
Author(s):  
Sen Guo ◽  
Yongsheng Liang ◽  
Zhili Zhang ◽  
Wei Liu
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Association Rule ◽  
Rough Set Theory ◽  
Web Log

scholarly journals AN INDISCERNIBILITY APPROACH FOR PRE PROCESSING OF WEB LOG FILES

2012 ◽  
pp. 231-234
Author(s):  
JEEVA JOSE ◽  
P. SOJAN LAL
Keyword(s):  
World Wide Web ◽  
Set Theory ◽  
Rough Set ◽  
World Wide ◽  
Rough Set Theory ◽  
Web Usage Mining ◽  
Web Log ◽  
Log Files ◽  
Challenging Tasks ◽  
And Behavior

World Wide Web has a spectacular growth not only in terms of the number of websites and volume of information, but also in terms of the number of visitors. Web log files contain tremendous information about the user traffic and behavior. A large amount of pre processing is required for eliminating the noise and is one of the challenging tasks in web usage mining. This paper proposes an indiscernibility approach in rough set theory for pre processing of web log files.

Approach for Mining Fault Rules of Power Grid Based on the Combination of Rough Set Theory and Association Rule

2013 ◽  
Vol 333-335 ◽  
pp. 693-697
Author(s):  
Yong Chao Liang ◽  
Xi Jia Zhang ◽  
Peng Zhou
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Association Rule ◽  
Rough Set Theory ◽  
Power Grid ◽  
Attribute Reduction ◽  
Novel Approach ◽  
Rules Extraction ◽  
Decision Tables ◽  
Fault Information

With the increasing of fault information transmission capacity in power grid, the volume of information which needs to be concerned by dispatchers has greatly increased, consequently making it difficult to identify the fault signal and analyze the cause of the accident quickly for dispatchers in massive fault information. To settle this problem, this paper uses a novel approach that combines rough set theory with association rule for mining fault rules in a large number of historical fault data of power grid. Firstly, it builds distributed original decision tables according to regions. And then it uses the information entropy algorithm in condition attribute reduction. Lastly, it applies the improved Apriori algorithm of association rule to fault rules mining based on the reduction decision table. In this way the problems of redundancy of massive fault information can be solved and complexity of rules extraction can be simplified effectively. It also improves the efficiency of fault rules mining.

Integrating rough set theory with customer satisfaction to construct a novel approach for mining product design rules

2021 ◽  
pp. 1-25
Author(s):  
Tianxiong Wang ◽  
Meiyu Zhou
Keyword(s):  
Customer Satisfaction ◽  
Product Design ◽  
Set Theory ◽  
Rough Set ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rough Set Theory ◽  
Mining Method ◽  
Rule Mining ◽  
Design Elements

When users choose a product, they consider the emotional experience triggered by the product form. In view of the fact that traditional kansei engineering can not effectively reflect the complex and changeable psychological factors of users, and it has not explored the complex relationship between customer satisfaction and perceptual demand characteristics through quantitative analysis. To address this problem, some uncertainty techniques including rough sets and fuzzy sets are applied to capture more accurate emotion knowledge. Therefore, this research proposes an integrated evaluation gird method (EGM), rough set theory (RST), continuous fuzzy kano model (CFKM), fuzzy weighted association rule mining method to extract the significant relationship between user needs and product morphological features. The EGM is applied to analyze the attractive factor of morphological characteristics of the product, and then the demand items with the highest satisfaction are analyzed through CFKM. The semantic difference method is combined to construct a decision table, and through attribute reduction and importance calculation to obtain the weight of the core product design items. In order to explore the non-linear relationship between design elements and kansei images, the fuzzy weighted association rule mining method was applied to obtain the set of frequent fuzzy weighted association rules based on evidence theory’s reliability indices of minimum support and confidence so as to realize user demand-driven product design. Taking the design of electric bicycle as an example, the experiment results show that the proposed method can help companies or designers develop products to generate good solutions for customer need.

A rough set theory application in forecasting models

2020 ◽  
Vol 3 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Haresh Sharma ◽  
◽  
Kriti Kumari ◽  
Samarjit Kar ◽  
◽  
...  
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Forecasting Models ◽  
Theory Application
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