Injecting Differential Privacy in Rules Extraction of Rough Set

2019 ◽  
pp. 175-187
Author(s):  
Xianxian Li ◽  
Chunfeng Luo ◽  
Peng Liu ◽  
Li-e Wang ◽  
Dongran Yu
Keyword(s):  
Rough Set ◽  
Differential Privacy ◽  
Rules Extraction

Research of Improved Attribute Reduction Algorithm Based on Data Mining of Rough Set

2014 ◽  
Vol 644-650 ◽  
pp. 2120-2123 ◽  
Author(s):  
De Zhi An ◽  
Guang Li Wu ◽  
Jun Lu
Keyword(s):  
Data Mining ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Reduction Algorithm ◽  
The Core ◽  
Rules Extraction

At present there are many data mining methods. This paper studies the application of rough set method in data mining, mainly on the application of attribute reduction algorithm based on rough set in the data mining rules extraction stage. Rough set in data mining is often used for reduction of knowledge, and thus for the rule extraction. Attribute reduction is one of the core research contents of rough set theory. In this paper, the traditional attribute reduction algorithm based on rough sets is studied and improved, and for large data sets of data mining, a new attribute reduction algorithm is proposed.

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.

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