On Association Rules Mining Algorithms with Data Privacy Preserving

Author(s):  
Marcin Gorawski ◽  
Karol Stachurski
2010 ◽  
Vol 108-111 ◽  
pp. 436-440
Author(s):  
Yue Shun He ◽  
Ping Du

This paper presents an adaptive support for Boolean algorithm for mining association rules, the Algorithm does not require minimum support from outside, in the mining process of the algorithm will be based on user needs the minimum number of rules automatically adjust the scope of support to produce the specific number of rules, the algorithm number of rules for the user needs to generate the rules to a certain extent, reduce excavation time, avoid the artificial blindness specified minimum support. In addition, the core of the algorithm is using an efficient method of Boolean-type mining, using the logical OR, AND, and XOR operations to generate association rules, to avoided the candidate itemsets generated In the mining process, and only need to scan the database once, so the algorithm has a certain efficiency.


2011 ◽  
Vol 130-134 ◽  
pp. 2629-2632
Author(s):  
Jie Liu ◽  
Tian Qi Li ◽  
Jian Pei Zhang

Multi-parameters data perturbation method is a kind of original data perturbation methods for privacy preserving association rules mining. However, the time-efficiency of restoring the frequent itemsets in multi-parameters perturbation algorithm is still not high.One method is proposed in this paper to improve the time efficiency of multi-parameters randomized perturbation algorithm according to the characteristics of the model to restore frequent itemsets. The method improves the time efficiency by getting the elements of the first line of the inversed matrix of transformation matrix. Finally, both theoretical analysis and experimental results show that the improved algorithm is more time-efficient and space-efficient than the original algorithm.


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